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From: [email protected] Date: November 20, 2005 2:21:58 PM PST To: [email protected] Subject: Web of Science Notes: Web of Science F ISI Export Format N V 1.0 R P T A U T I S O A B J Martinez, D Detailed and abstract phase-locked attractor network models of early olfac BIOLOGICAL CYBERNETICS Across species, primary olfactory centers show similarities both in their ce types of olfactory information coding. In this article, we consider an excitat network as a model of early olfactory systems (antennal lobe for insects, olf In line with experimental results, we show that, in our network, odor-like of excitatory cells, phase-locked to the oscillations of the local field p mathematical analysis, the phase-locking probability of excitatory cells is g and the firing probability of inhibitory cells is well described by a sigmoid f functions are used to reduce the spiking model to a more abstract model wi (oscillatory cycles) and binary-state neurons (phase-locked or not). An itera the dynamics of the binary model, reveals that it converges to fixed point obtained with the spiking model. This result is consistent with odor-specif experimental studies. It also provides insights for designing bio-inspired o applicable for data analysis in electronic noses. 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In this model t into more large groups (domains), and accordingly the updating rule is modi capacity grows linearly as function of the domain size. In optimization task allows one to find more deep local minima of the energy than the standard C ABUMOSTAFA YS, 1985, IEEE T INFORM THEORY, V31, P461 R AMIT DJ, 1987, ANN PHYS-NEW YORK, V173, P30 COHEN MA, 1983, IEEE T SYST MAN CYB, V13, P815 GROSSBERG S, 1987, ADAPPTIVE BRAIN, V1 HEBB D, 1949, ORG BEHAV HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 KINZEL W, 1985, Z PHYS B CON MAT, V60, P205 KRYZHANOVSKII BV, 2003, DOKL MATH, V67, P455 KRYZHANOVSKII BV, 2005, DOKL MATH, V71, P310 PALM G, 1992, NETWORK-COMP NEURAL, V3, P177 N 10 R P 2005 Y V 3697 L B 397 P E 403 P U ISI:000232196000063 T E R P T A U T I S O A B J Bair, W Visual receptive field organization CURRENT OPINION IN NEUROBIOLOGY Increasingly systematic approaches to quantifying receptive fields in primar inspired ideas about functional circuitry, non-linearities, and visual stimu to classical problems. This includes the distinction and hierarchy between mechanisms underlying the receptive field surround, and debates about optimal fields. An important new problem arises from recent observations of stimulussummation in primary visual cortex. It appears that the receptive field can n and we might have to relinquish this cherished notion as the embodiment of visual cortex. C ALBRECHT DG, 2003, VISUAL NEUROSCIENCES, P747 R ALBRIGHT TD, ANN REV NEUROSCI, V25, P339 ANGELUCCI A, 2002, J NEUROSCI, V22, P8633 ANGELUCCI A, 2003, J PHYSIOLOGY-PARIS, V97, P141 BAIR W, 2003, J NEUROSCI, V23, P7690 BAIR W, 2004, J NEUROSCI, V24, P7305 BROWN HA, 2003, VISUAL NEUROSCI, V20, P85 CARANDINI M, 2000, J NEUROSCI, V20, P470 CARDIN JA, 2005, J NEUROSCI, V25, P5339 CAVANAUGH JR, 2002, J NEUROPHYSIOL, V88, P2530 DAVID SV, 2004, J NEUROSCI, V24, P6991 GILBERT CD, 1996, P NATL ACAD SCI USA, V93, P615 GUO K, 2005, EUR J NEUROSCI, V21, P536 HIRSCH JA, 1991, J NEUROSCI, V11, P1800 HIRSCH JA, 2003, NAT NEUROSCI, V6, P1300 HUBEL DH, 1962, J PHYSIOL-LONDON, V160, P106 IBBOTSON MR, 2005, J NEUROPHYSIOL, V93, P3699 KAPADIA MK, 1999, P NATL ACAD SCI USA, V96, P12073 KAYSER C, 2003, J NEUROPHYSIOL, V90, P1910 LAURITZEN TZ, 2003, J NEUROSCI, V23, P10201 MARTINEZ LM, 2005, NAT NEUROSCI, V8, P372 MATA ML, 2005, J NEUROPHYSIOL, V93, P919 MECHLER F, 2002, VISION RES, V42, P1017 NOLT MJ, 2004, J NEUROPHYSIOL, V92, P1708 NOWAK LG, 2005, J NEUROSCI, V25, P1866 OZEKI H, 2004, J NEUROSCI, V24, P1428 PRIEBE NJ, 2004, NAT NEUROSCI, V7, P1113 RINGACH DL, 2004, J NEUROPHYSIOL, V92, P468 RINGACH DL, 2004, J PHYSIOL-LONDON, V558, P717 ROBERTS MJ, 2005, J NEUROPHYSIOL, V93, P2062 SCENIAK MP, 1999, NAT NEUROSCI, V2, P733 SCENIAK MP, 2002, J NEUROPHYSIOL, V88, P1363 SKOTTUN BC, 1991, VISION RES, V31, P1079 SMYTH D, 2003, J NEUROSCI, V23, P4746 SOLOMON SG, 2002, J NEUROSCI, V22, P338 STETTLER DD, 2002, NEURON, V36, P739 TAO L, 2004, P NATL ACAD SCI USA, V101, P366 TOURYAN J, 2005, NEURON, V45, P781 TROYER TW, 1998, J NEUROSCI, V18, P5908 USREY WM, 2003, J NEUROPHYSIOL, V89, P1003 VANDERSMAGT MJ, 2005, IN PRESS J NEUROPHYS WELIKY M, 2003, NEURON, V37, P703 WIELAARD J, 2005, EXTRACLASSICAL RECEP ZHANG B, 2005, P NATL ACAD SCI USA, V102, P5862 N R P D P Y V L I S B P E P U T E R 44 AUG 2005 15 4 459 464 ISI:000231660000012 P J T A Marrelec, G U Daunizeau, J Pelegrini-Issac, M Doyon, J Benali, H T Conditional correlation as a measure of mediated interactivity in fMRI and I S IEEE TRANSACTIONS ON SIGNAL PROCESSING O A Many measures have been proposed so far to extract brain functional interacti B resonance imaging (fMRI) and magnetoencephalography/electroencephalography Unfortunately, none has been able to provide a relevant, self-contained, a interaction. In this paper, we propose a first step in this direction. We first together with a cross-modal definition of interaction. In this setting, we shared by some measures of interaction proposed in the literature. We show nonlinear correlation, mutual information, generalized synchronization, phas and phase locking value (PLV) actually measure the same quantity (namely c investigating linear interactions between independently and identically di We also demonstrate that these data-driven measures can only partly account that can be expressed by the effective connectivity of structural equation m gap, we suggest the use of conditional correlation, which is shown to be rel C ALLEFELD C, 2004, INT J BIFURCAT CHAOS, V14, P417 R ANDERSON TW, 1958, INTRO MULTIVARIATE S ARFANAKIS K, 2000, MAGN RESON IMAGING, V18, P921 ARNHOLD J, 1999, PHYSICA D, V134, P419 ATTWELL D, 2002, TRENDS NEUROSCI, V25, P621 AUBERT A, 2002, NEUROIMAGE, V17, P1162 BABILONI F, 2004, INT CONGR SER, V1270, P126 BARTOLOMEI F, 2001, CLIN NEUROPHYSIOL, V112, P1746 BELLEC P, 2004, P IEEE ISBI, P848 BISWAL B, 1995, MAGNET RESON MED, V34, P537 BISWAL BB, 1997, NMR BIOMED, V10, P165 BOCCALETTI S, 2002, PHYS REP, V366, P1 BULLMORE E, 2000, NEUROIMAGE, V11, P289 BUZUG T, 1994, PHYSICA D, V72, P343 CHAVEZ M, 2003, J NEUROSCI METH, V124, P113 CHEN W, 1999, FUNCTIONAL MRI, P103 CORDES D, 2000, AM J NEURORADIOL, V21, P1636 DASILVA FHL, 1980, ELECTROEN CLIN NEURO, V50, P449 DAVID O, 2002, NEUROIMAGE, V17, P1277 DAVID O, 2003, NEUROIMAGE, V20, P1743 DAVID O, 2004, NEUROIMAGE, V21, P659 FRACKOWIAK RSJ, 2003, HUMAN BRAIN FUNCTION FRISTON KJ, 1993, HUM BRAIN MAPP, V1, P69 FRISTON KJ, 1993, J CEREBR BLOOD F MET, V13, P5 FRISTON KJ, 1994, HUMAN BRAIN MAPPING, V1, P153 FRISTON KJ, 1995, NEUROIMAGE, V2, P45 FRISTON KJ, 1997, HUM BRAIN MAPP, V5, P48 FRISTON KJ, 2001, BRAIN RES BULL, V54, P275 FRISTON KJ, 2001, SCAND J PSYCHOL, V42, P167 FRISTON KJ, 2003, NEUROIMAGE, V19, P1273 GAVRILESCU M, 2004, HUM BRAIN MAPP, V21, P49 GONZALEZLIMA F, 1995, MATH COMPUT SIMULAT, V40, P115 GRAY RM, 1990, ENTROPY INFORM THEOR GROSS J, 2001, P NATL ACAD SCI USA, V98, P694 HAMALAINEN M, 1993, REV MOD PHYS, V65, P413 HORWITZ B, 2003, NEUROIMAGE 1, V19, P466 HUETTEL SA, 2004, FUNCTIONAL MAGNETIC HUMBERTDROZ H, 2001, MEMOIRE DIPLOME DETU, V70 JAYNES ET, 2003, PROBABILITY THEORY L, V1 JEFFREYS H, 1939, THEORY PROBABILITY JEONG J, 2001, CLIN NEUROPHYSIOL, V112, P827 JONES M, 2004, NEUROIMAGE, V22, P956 KAMINSKI M, 2001, BIOL CYBERN, V85, P145 KOTTER R, 2000, PHILOS T ROY SOC B, V355, P127 LACHAUX JP, 1999, HUM BRAIN MAPP, V8, P194 LAHAYE PJ, 2003, NEUROIMAGE, V20, P962 LEE L, 2003, NEUROIMAGE 1, V19, P457 LEVANQUYEN M, 1999, PHYSICA D, V127, P250 LI TQ, 2000, NEUROIMAGE, V12, P442 LOGOTHETIS NK, 2001, NATURE, V412, P150 LUMER ED, 1997, CEREB CORTEX, V7, P207 MACKAY DJC, 2003, INFORM THEORY INFERE MAKIRANTA MJ, 2004, NEUROIMAGE, V22, P222 MALMIVUO J, 1981, BIOELECTROMAGNETISM MARTINEZMONTES E, 2004, NEUROIMAGE, V22, P1023 MCINTOSH AR, 1996, NEUROIMAGE 1, V3, P143 MORMANN F, 2000, PHYSICA D, V144, P358 NA SH, 2002, CLIN NEUROPHYSIOL, V113, P1954 NIKI K, 2000, NEUROIMAGE, V11, S484 NUNEZ PL, 1981, ELECT FIELDS BRAIN NUNEZ PL, 1997, ELECTROEN CLIN NEURO, V103, P499 NUNEZ PL, 2000, BRAIN TOPOGR, V13, P79 PENNY WD, 2004, NEUROIMAGE S1, V23, S264 PENNY WD, 2004, NEUROIMAGE, V22, P1157 PERLBARG V, 2004, P IEEE ISBI, P852 RAMNANI N, 2004, BIOL PSYCHIAT, V56, P613 RIEHLE A, 1997, SCIENCE RULKOV NF, 1995, PHYS REV E, V51, P980 SARVAS J, 1987, PHYS MED BIOL, V32, P11 SHULMAN RG, 2004, TRENDS NEUROSCI, V27, P8 SPORNS O, 2004, TRENDS COGN SCI, V8, P418 SRINIVASAN R, 1999, J NEUROSCI, V19, P5435 STAM CJ, 1996, ELECTROEN CLIN NEURO, V99, P24 STONE JV, 2002, TRENDS COGN SCI, V6, P327 SUN FT, 2004, NEUROIMAGE, V21, P647 TALLONBAUDRY C, 1998, J NEUROSCI, V18, P4244 TEDESCHI W, 2004, PHYSICA A, V344, P705 TREISMAN A, 1996, CURR OPIN NEUROBIOL, V6, P171 TRUJILLOBARRETO NJ, 2001, INT J BIOELECTROMAG, V3 VARELA F, 2001, NAT REV NEUROSCI, V2, P229 WENDLING F, 2003, BRAIN 6, V126, P1449 XIONG JH, 1999, HUM BRAIN MAPP, V8, P151 XU JH, 1997, PHYSICA D, V106, P363 N R P D P Y V L I S B P E P U T E R P T A U T I S O S E A B 83 SEP 2005 53 9 3503 3516 ISI:000231318900014 S Knoblauch, A Pulvermuller, F Sequence detector networks and associative learning of grammatical categor BIOMIMETIC NEURAL LEARNING FOR INTELLIGENT ROBOTS: INTELLIGENT SYSTEMS, CO NEUROSCIENCE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE A fundamental prerequisite for language is the ability to distinguish word seq well-formed from ungrammatical word strings and to generalise rules of syntact of constituents. In this work, we extend a neural model of syntactic brain syntactic sequence detectors (SDs). Elementary SDs are neural units that spec of constituent words AB, but not (or much less) to the reverse sequence BA. original version of the SD model (Pulvermuller, Theory in Biosciences, 200 variants taking advantage of optimised neuronal response functions, non-line and leaky integration of neuronal input accumulating over time. A biologicall including a network of several SDs is used to demonstrate that associative leads to learning of word sequences, formation of neural representations o linking of sequence detectors into neuronal assemblies that may provide a rule knowledge. We propose that these syntactic neuronal assemblies (SNAs) syntactic regularities from already encountered strings to new grammatical C BRAITENBERG V, 1997, BEHAV BRAIN SCI, V20, P229 R CHOMSKY N, 1957, SYNTACTIC STRUCTURES DESTEXHE A, KINETIC MODELS SYNAP, CH1 EGELHAAF M, 1989, J OPT SOC AM A, V6, P1070 ELMAN J, 1996, RETHINKING INNATENES ELMAN JL, 1990, COGNITIVE SCI, V14, P179 FUSTER J, 1999, MEMORY CEREBRAL CORT HAUSER MD, 2002, SCIENCE, V298, P1569 KLEENE SC, 1956, AUTOMATA STUDIES, P3 KNOBLAUCH A, 2001, NEURAL NETWORKS, V14, P763 KNOBLAUCH A, 2002, NEUROCOMPUTING, V44, P19 KNOBLAUCH A, 2003, THESIS U ULM GERMANY KNOBLAUCH A, 2005, UNPUB ASSOCIATIVE LE KOCH C, 1998, METHODS NEURONAL MOD PALM G, 1980, BIOL CYBERN, V36, P19 PALM G, 1982, NEURAL ASSEMBLIES AL PULVERMULLER F, 2002, PROG NEUROBIOL, V67, P85 PULVERMULLER F, 2003, NEUROSCIENCE LANGUAG PULVERMULLER F, 2003, THEOR BIOSCI, V122, P87 PULVERMULLER F, 2005, UNPUB EMERGENCE DISC REICHARDT W, 1959, Z NATURFORSCH B, V14, P674 N R P Y V L B P E P U T E R 21 2005 3575 31 53 ISI:000230754900003 P S T A Markert, H U Knoblauch, A Palm, G T Detecting sequences and understanding language with neural associative mem I S BIOMIMETIC NEURAL LEARNING FOR INTELLIGENT ROBOTS: INTELLIGENT SYSTEMS, CO O NEUROSCIENCE S LECTURE NOTES IN ARTIFICIAL INTELLIGENCE E A Using associative memories and sparse distributed representations we have dev B to associate words with objects, properties like colors, and actions. This context to enable a robot to respond to spoken commands like "bot show plum cup". This involves parsing and understanding of simple sentences and "sym relating the nouns to concrete objects sensed by the camera and recognized visual input. C BRAITENBERG V, 1978, LECTURE NOTES BIOMAT, V21, P171 R CHOMSKY N, 1957, SYNTACTIC STRUCTURES FAY R, COMBINING VISUAL ATT FAY R, 2004, 3 WORKSH SOAVE2004 S FAY R, 2004, IN PRESS ULM NEURORO HEBB D, 1949, ORG BEHAV NEUROPSYCH HOPCROFT J, 1969, FORMAL LANGUAGES THE HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 KNOBLAUCH A, SEQUENCE DETECTOR NE KNOBLAUCH A, 2001, NEURAL NETWORKS, V14, P763 KNOBLAUCH A, 2003, THESIS U ULM GERMANY KNOBLAUCH A, 2004, 403 AAAI, P10 PALM G, 1980, BIOL CYBERN, V36, P19 PALM G, 1982, CYBERNETICS SYSTEMS PALM G, 1982, NEURAL ASSEMBLIES AL PALM G, 1987, REAL BRAINS ARTIFICA PALM G, 1990, BRAIN THEORY PALM G, 1990, CONCEPTS NEUROSCI, V1, P133 PALM G, 1991, CONCEPTS NEUROSCI, V2, P97 PULVERMULLER F, 1999, BEHAV BRAIN SCI, V22, P253 PULVERMULLER F, 2003, NEUROSCIENCE LANGUAG RIZZOLATTI G, 1999, ARCH ITAL BIOL, V137, P85 SCHWENKER F, 1996, NEURAL NETWORKS, V9, P445 SOMMER FT, 1999, NEURAL NETWORKS, V12, P281 WERMTER S, 2005, BIOMIMETIC NEURAL LE WILLSHAW DJ, 1969, NATURE, V222, P960 N 26 R P 2005 Y V 3575 L B 107 P E 117 P U ISI:000230754900007 T E R P S T A Fay, R U Kaufmann, U Knoblauch, A Markert, H Palm, G T Combining visual attention, object recognition and associative information I system S BIOMIMETIC NEURAL LEARNING FOR INTELLIGENT ROBOTS: INTELLIGENT SYSTEMS, CO O NEUROSCIENCE S LECTURE NOTES IN ARTIFICIAL INTELLIGENCE E A We have implemented a neurobiologically plausible system on a robot that integ B recognition, language and action processing using a coherent cortex-like a associative memories. This system enables the robot to respond to spoken c or "bot put apple to yellow cup". The scenario for this is a robot close t certain kinds of fruit and other simple objects. Tasks such as finding and a complex visual scene according to spoken or typed commands can be demons and understanding of simple sentences, relating the nouns to concrete obje coordinating motor output with planning and sensory processing. 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Because the extraction of c complex, we apply an approximative method which is based on artificial neur find clusters of various degrees of connectivity ranging between the two e connectivity components. We propose a criterion which allows to evaluate the based on the robustness with respect to parameter variations. Exploiting t we can show that regions of substantial correlation with an external stimulus from other activity. 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This simple outline of feedforward connection followed by visual information through cat primary visual cortex. We studied among physiologically identified neurons recorded at different depths within columns using multielectrode recordings and cross-correlation analysis. Our that the projection from layer 4 to layers 2+3 is actually split in parallel that are defined by differences in the dynamic properties of the connections an decode the temporal information embedded on every spike train. 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The results from seven normal volunteers show th for discriminating vessels and compartments in the pulmonary circulation. Pa of acquired or congenital pulmonary perfusion disorders demonstrate that p in a concise map that combines information of the mean transit time (MTT) and The method was found to provide greater insight into the perfusion dynamic current model-based analyses, and may serve as a basis for optimal hemodyn the interrogated perfusion compartments. C BAUNE A, 1999, NEUROIMAGE, V9, P477 R BEZDEK JC, 1981, PATTERN RECOGNITION CARROLL TJ, 2002, AM J NEURORADIOL, V23, P1007 CHUANG KH, 1999, IEEE T MED IMAGING, V18, P1117 FRATZ S, 2002, CIRCULATION, V106, P1510 HARALDSETH O, 1999, MAGMA, V8, P146 HATABU H, 1996, MAGNET RESON MED, V36, P503 HATABU H, 1999, MAGNET RESON MED, V42, P1033 HOWARTH NR, 1999, EUR RADIOL, V9, P1574 KAUCZOR HU, 2000, EUR J RADIOL, V34, P196 KOHONEN T, 1995, SELF ORG MAPS LEVIN DL, 2001, MAGNET RESON MED, V46, P166 MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P160 MURASE K, 2001, J MAGN RESON IMAGING, V13, P797 PAL NR, 1993, IEEE T NEURAL NETWOR, V4, P549 ROSEN BR, 1990, MAGNET RESON MED, V14, P249 SCARTH GB, 1995, P INT SOC MAGN RES M, P238 SLAVIN GS, 2001, RADIOLOGY, V219, P258 THOMAS DL, 2000, PHYS MED BIOL, V45, R97 TORHEIM G, 2001, J MAGN RESON IMAGING, V13, P577 UEMATSU H, 2001, EUR J RADIOL, V37, P155 N 21 R P AUG D P 2005 Y V L I S B P E P U T E R P T A U T I S O A B 54 2 299 308 ISI:000230765700007 J Alonso, JM Swadlow, HA Thalamocortical specificity and the synthesis of sensory cortical receptiv JOURNAL OF NEUROPHYSIOLOGY A persistent and fundamental question in sensory cortical physiology concerns fields of layer-4 neurons are synthesized from their thalamic inputs. Acco proposed more than 40 years ago, simple receptive fields in layer 4 of pri from the convergence of highly specific thalamocortical inputs (e.g., geni receptive fields overlap the ON subregions of layer 4 simple cells). Here, visual cortex that provide support for this high specificity of thalamic in cells. In addition, we review studies of GABAergic interneurons in the somat receptive fields that are generated by a very different mechanism: the nonspe inputs with different response properties. We hypothesize that these 2 modes of onto subpopulations of excitatory and inhibitory neurons constitute a genera and account for much of the diversity seen in layer-4 receptive fields. C ABELES M, 1991, CORTICONICS NEURAL C R ALONSO JM, 2001, J NEUROSCI, V21, P4002 AMITAI Y, 2002, J NEUROSCI, V22, P4142 AZOUZ R, 1997, CEREB CORTEX, V7, P534 BEIERLEIN M, 2003, J NEUROPHYSIOL, V90, P2987 BENYISHAI R, 1995, P NATL ACAD SCI USA, V92, P3844 BRUNO RM, 2002, J NEUROSCI, V22, P10966 CHUNG S, 1998, NEURON, V20, P1177 CONTRERAS D, 2003, J NEUROSCI, V23, P6936 DOUGLAS RJ, 1995, SCIENCE, V269, P981 FAIREN A, 1984, CEREB CORTEX, V1, P201 FERSTER D, 1983, J PHYSIOL-LONDON, V342, P181 FERSTER D, 1986, J NEUROSCI, V6, P1284 FERSTER D, 1996, NATURE, V380, P249 FERSTER D, 2000, ANNU REV NEUROSCI, V23, P441 GALARRETA M, 1999, NATURE, V402, P72 GIBSON JR, 1999, NATURE, V402, P75 GILBERT CD, 1979, NATURE, V280, P120 GONCHAR Y, 1997, CEREB CORTEX, V7, P347 GRIFFITH JS, 1963, BIOPHYS J, V3, P299 GRIFFITH JS, 1967, NATURE, V215, P1043 GUPTA A, 2000, SCIENCE, V287, P273 HIRSCH JA, 1998, J NEUROSCI, V18, P9517 HIRSCH JA, 2003, NAT NEUROSCI, V6, P1300 HOUSER CR, 1983, J NEUROCYTOL, V12, P617 HUBEL DH, 1962, J PHYSIOL-LONDON, V160, P106 KAWAGUCHI Y, 1997, CEREB CORTEX, V7, P476 KELLY JP, 1974, J PHYSIOL-LONDON, V238, P515 LAMPL I, 2001, NEURON, V30, P263 LAURITZEN TZ, 2003, J NEUROSCI, V23, P10201 LEVICK WR, 1972, INVEST OPHTHALMOL, V11, P302 MARKRAM H, 2004, NAT REV NEUROSCI, V5, P793 MARTIN KAC, 1984, J PHYSIOL-LONDON, V353, P463 MARTINEZ LM, 2002, J PHYSIOL-LONDON, V540, P321 MASTRONARDE DN, 1987, J NEUROPHYSIOL, V57, P381 MCMULLEN NT, 1994, BRAIN RES, V660, P225 MILLER KD, 2001, CURR OPIN NEUROBIOL, V11, P488 MILLER KD, 2003, CEREB CORTEX, V13, P73 MILLER LM, 2001, NEURON, V32, P151 MONIER C, 2003, NEURON, V37, P663 MOUNTCASTLE VB, 1969, J NEUROPHYSIOL, V32, P452 PETERS A, 1993, CEREB CORTEX, V3, P69 PINTO DJ, 2003, CEREB CORTEX, V13, P33 REID RC, 1995, NATURE, V378, P281 REN JQ, 1992, EXP BRAIN RES, V92, P1 RINGACH DL, 2004, J NEUROPHYSIOL, V92, P468 SHAPLEY R, 2003, NEURON, V38, P689 SHERMAN SM, 2001, EXPLORING THALAMUS SIMONS DJ, 1978, J NEUROPHYSIOL, V41, P798 SOMERS DC, 1995, J NEUROSCI, V15, P5448 SWADLOW HA, 1987, J NEUROPHYSIOL, V57, P977 SWADLOW HA, 1988, J NEUROPHYSIOL, V59, P1162 SWADLOW HA, 1989, J NEUROPHYSIOL, V62, P288 SWADLOW HA, 1991, J NEUROPHYSIOL, V66, P1392 SWADLOW HA, 1994, J NEUROPHYSIOL, V71, P437 SWADLOW HA, 1995, J NEUROPHYSIOL, V73, P1584 SWADLOW HA, 1998, J NEUROPHYSIOL, V79, P567 SWADLOW HA, 2002, NAT NEUROSCI, V5, P403 SWADLOW HA, 2003, CEREB CORTEX, V13, P25 TANAKA K, 1983, J NEUROPHYSIOL, V49, P1303 TOLHURST DJ, 1987, EXP BRAIN RES, V66, P607 USREY WM, 1999, J NEUROPHYSIOL, V82, P3527 WESTHEIMER G, 1977, VISION RES, V17, P941 WHITE EL, 1989, CORTICAL CIRCUITS WOOLSEY TA, 1970, BRAIN RES, V17, P205 WOOLSEY TA, 1975, J COMP NEUROL, V164, P79 N R P D P Y V L I S B P E P U T E R 66 JUL 2005 94 1 26 32 ISI:000230135500005 P J T A Fell, J U Elger, CE Fernandez, G T Mediotemporal gamma activity and declarative memory I S KLINISCHE NEUROPHYSIOLOGIE O A Purpose: Human declarative memory, i.e. the consciously accessible long-ter B crucially depends on two structures within the medial temporal lobe (MTL), hippocampus. However, so far there was no direct evidence for an interaction during memory formation. Transient coupling of neural assemblies can be ac synchronisation of gamma activity, i.e. EEG activity in the frequency range a of 9 patients with unilateral MTL-epilepsies the event-related EEG was recorde a word-memory task. The EEG responses of the non-pathological MTL for later r were compared. Additionally, the continuous EEG was recorded during sleep wi in a group of 8 patients. Phase synchronisation and spectral coherence wer a neural coupling. Results: Successful memory formation is accompanied by increase of phase synchronisation between rhinal cortex and hippocampus an memory-related synchronisation changes are interindividually correlated wi rhinal-hippocampal theta (4-7Hz) coherence. Compared to the waking state r decreases during sleep, most pronounced within the gamma-band. Discussion: T probably reflect a slowly modulated connectivity between rhinal cortex and synchronisation accomplishes the fast coupling and decoupling processes, whic the information transfer between both structures. The reduced rhinal-hippo may represent an indirect electrophysiological correlate of the diminished abi sleep. 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It is shown that the variance in the postsynaptic pot of the stimulation strength if the synapses have been generated by Hebbian patterns, but only linearly for independent random synapses. This result b analysis of associative memory and the detection of Hebbian cell assemblie experiments. (c) 2004 Elsevier B.V. All rights reserved. C BRAITENBERG V, 1991, ANATOMY CORTEX STAT R BUCKINGHAM J, 1992, NETWORK-COMP NEURAL, V3, P407 BUCKINGHAM JT, 1991, THESIS U EDINBURGH HEBB DO, 1949, ORG BEHAV KNOBLAUCH A, 2001, NEURAL NETWORKS, V14, P763 KNOBLAUCH A, 2003, LECT NOTES COMPUT SC, V2714, P325 KNOBLAUCH A, 2003, THESIS U ULM GERMANY NADAL JP, 1991, J PHYS A-MATH GEN, V24, P1093 PALM G, 1980, BIOL CYBERN, V36, P19 PALM G, 1982, NEURAL ASSEMBLIES AL PALM G, 1990, CONCEPTS NEUROSCI, V1, P133 PALM G, 1991, CONCEPTS NEUROSCI, V2, P97 SOMMER FT, 1999, NEURAL NETWORKS, V12, P281 STEINBUCH K, 1961, KYBERNETIK, V1, P36 WILLSHAW DJ, 1969, NATURE, V222, P960 N R P D P Y V L B P E P U T E R P T A U T I S O A B 15 JUN 2005 65 647 652 ISI:000229663600084 J Hutt, A Atay, FM Analysis of nonlocal neural fields for both general and gamma-distributed PHYSICA D-NONLINEAR PHENOMENA This work studies the stability of equilibria in spatially extended neuron the model equation from statistical properties of the neuron population. The equation includes synaptic and space-dependent transmission delay for both synaptic connectivities. The latter connectivity type reveals infinite, fi self-connectivities. The work derives conditions for stationary and nonstat kernel types. In addition, a nonlinear analysis for general kernels yields of the Turing instability. To compare the results to findings for partial d two typical PDE-types are derived from the examined model equation, namely t equation and the Swift-Hohenberg equation. Hence, the discussed integro-diff these PDEs. In the case of the gamma-distributed kernels, the stability cond of the mean excitatory and inhibitory interaction ranges. As a novel finding, in fields with local inhibition-lateral excitation, while wave instabiliti excitation and lateral inhibition. Numerical simulations support the analy Published by Elsevier B.V. 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In cat visual c simultaneous recordings from > 100 well-isolated neurons. Using standard c provide a quality of single-unit isolation that surpasses that attainable successful in vivo recording and precise electrode positioning are describ high-bandwidth continuous data-acquisition system designed specifically fo impedance meter for testing polytrode site integrity. Despite having small earlier silicon-based electrodes of this type, these polytrodes have negli comparable reliability, and low site impedances and are capable of making highwith minimal tissue damage. The relatively benign nature of planar electro histologically and in experiments where the polytrode was repeatedly advan microns over periods of many hours. It was possible to maintain stable rec adjacent to the polytrode without change in their absolute positions, neuroph properties. 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This model is shown to be a direct generalization of Aristoteli related to a calculable quantity. A key aspect of this model is that in Ar environments it functions logically. However, in non-Aristotelian environm conclusion with the highest probability of being true (a popular past model of functions in the manner of the 'duck test;' by finding that conclusion whi truth of the assumed facts. (c) 2004 Elsevier Ltd. All rights reserved. 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PET studies of regional cerebral blood flow duri cognitive functions (i.e. memory, speech, attention) hove shed light on pa specialised brain regions and on a systems level in normal volunteers as well a impact in the diagnosis and differential diagnosis of neurodegenerative di psychiatric diseases. More recent developments include the development of trac and quantitation of beta-amyloid-plaques in patients suffering from Alzhei clinical implications within the framework of drug development and therapy C AGDEPPA ED, 2003, MOL IMAGING BIOL, V5, P404 R BARTENSTEIN P, 2002, EUR J NUCL MED MOL I, V29, BP43 FOX PT, 1984, J NEUROPHYSIOL, V51, P1109 FRISTON KJ, 1993, J CEREBR BLOOD F MET, V13, P5 FRISTON KJ, 1995, HUMAN BRAIN MAPPING, V2, P165 GJEDDE A, 1995, PRINCIPLES NUCL MED, P451 GLATTING G, 2004, MED PHYS, V31, P902 HAUTZEL H, 2003, NUKLEARMED-NUCL MED, V42, P197 HERHOLZ K, 2002, NEUROIMAGE, V17, P302 HERZOG H, 1996, J CEREBR BLOOD F MET, V16, P645 HORWITZ B, 1994, HUM BRAIN MAPP, V1, P269 HORWITZ B, 1994, HUMAN BRAIN MAPPING, V2, P112 KLUNK WE, 2001, LIFE SCI, V69, P1471 KLUNK WE, 2004, ANN NEUROL, V55, P306 KRAUSE BJ, 2000, IMAGING NEURAL MODEL, V13, P847 KRAUSE BJ, 2003, BILDGEBUNG GEHIRNS K KUWERT T, 1998, NERVENARZT, V69, P1045 MATHIS CA, 2003, J MED CHEM, V46, P2740 MEYER PT, 2003, EUR J NUCL MED MOL I, V30, P951 MOTTAGHY FM, 2004, FUNKTIONELLE BILDGEB, P3 NORDBERG A, 2004, LANCET NEUROL, V3, P519 PATLAK CS, 1983, J CEREBR BLOOD F MET, V3, P1 PHELPS ME, 1981, SCIENCE, V211, P1445 ROY CS, 1890, J PHYSIOL-LONDON, V11, P85 SCHRECKENBERGER M, 2003, BILDGEBUNG GEHIRNS K TALAIRACH J, 1988, CO PLANAR STEREOTAXI WOODS RP, 1992, J COMPUT ASSIST TOMO, V16, P620 ZEKI S, 1990, SIGNAL SENSE LOCAL G, P85 N 28 R P 2005 Y V 24 L I 2 S B 73 P E + P U ISI:000228217700002 T E R P T A U T I S O A B J Tao, HW Poo, MM Activity-dependent matching of excitatory and inhibitory inputs during ref fields NEURON The receptive field (RF) of single visual neurons undergoes progressive re It remains largely unknown how the excitatory and inhibitory inputs on single in a coordinated manner to allow the formation of functionally correct cir voltage-clamp recording from Xenopus tectal neurons, we found that RFs det inhibitory inputs in more mature tectal neurons are spatially matched, wit balanced synaptic excitation and inhibition. This emerges during developmen in the RF size and a transition from disparate to matched topography of exc to the tectal neurons. Altering normal spiking activity of tectal neurons by GABAA receptor activity significantly impeded the developmental reduction an Thus, appropriate inhibitory activity is essential for the coordinated ref inhibitory connections. 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The synaptic connections between the input and the R connections and the delays are adapted during an unsupervised learning proc in this network is modeled by a learning automaton. The action of the auto connection is considered as the delay of the corresponding synaptic connec simulations that the clustering precision of the proposed network is conside existing similar neural networks. (c) 2004 Elsevier B.V. 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Using combinatorial arguments, we d capacity. The capacity allowed by the dynamics of the system, alpha(C), is HCA, we obtain alpha(C) of order 0.1, and for SFC, we find values of order The capacity can be improved by introducing shadow patterns, inhibitory ce the excitatory assemblies in both memory models. This leads to a doubly balanc to the usual global balancing of excitation and inhibition, there exists specif of both types of assemblies on the background activity of the network. For for each network architecture, we obtain an allowed region ( phase space) for is viable. 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This equivalence of the two pattern masks ind between the plaid components was perfectly linear prior to the masking sta masks did not drive the detecting mechanism above its detection threshold facilitation by the pedestal (Foley, 1994). Therefore, the pattern masking within-channel masking, suggesting that linear summation of contrast signa suppressive contrast gain pool. We present a quantitative model of the effect for neurophysiological models of the process. 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The comp B across a cortical area yet are carried out by networks that can vary widel its functional architecture. Here we demonstrate a mechanism by which orient invariantly in cat primary visual cortex across an orientation preference map of local circuits. Visually evoked excitatory and inhibitory synaptic conduct in cortical neurons and thus keep the spike response sharply tuned at all m balance derives from spatially isotropic local connectivity of both excita Modeling results demonstrate that such covariation is a signature of recur feed-forward processing and that the observed isotropic local circuit is suf spike tuning. 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Such experiments can, for example, localize brain r stimuli, but frequently the spatiotemporal characteristics of the cerebral and variable, and thus difficult to evaluate using hypothesis-based methods in the temporal dimension to group voxels with similar time courses based on a n transform (DWT) representation of each time course. Applying the DWT to each information into coefficients associated with both time and scale. Discardi associated with high-frequency oscillations (noise) provided a straight-fo decreased the computational burden. Optimization-based clustering was then ap coefficients in order to produce a finite number of voxel clusters. This w (WCA) was evaluated using two representative classes of MRI neuroimaging exper MRI, following occlusion of the middle cerebral artery (MCAO), WCA differe different regions within the ischemic hemisphere. Following an acute cocaine c differences in the pharmacokinetic profile of the cerebral response. We concl method for blind analysis of time series image data. (C) 2004 Elsevier Inc C BAUDELET C, 2003, MAGNET RESON MED, V49, P985 R BAUMGARTNER R, 2000, MAGN RESON IMAGING, V18, P845 BAUNE A, 1999, NEUROIMAGE, V9, P477 BEALE EML, 1969, P 37 SESS LOND VOORB, V2, P92 BLOOM AS, 1999, HUM BRAIN MAPP, V8, P235 CALHOUN VD, 2001, NEUROIMAGE, V14, P1080 CALHOUN VD, 2002, MAGNET RESON MED, V48, P180 CALINSKI T, 1974, COMMUN STAT, V3, P1 CARANO RAD, 2000, J MAGN RESON IMAGING, V12, P842 CHEN YCI, 1997, MAGNET RESON MED, V38, P389 DAUBECHIES I, 1992, 10 LECT WAV, V61 DONOHO DL, 1994, BIOMETRIKA, V81, P425 DONOHO DL, 1995, J AM STAT ASSOC, V90, P1200 EVERITT BS, 2001, CLUSTER ANAL FILZMOSER P, 1999, MAGN RESON IMAGING, V17, P817 FRIMAN O, 2002, NEUROIMAGE, V16, P454 GENCAY R, 2001, INTRO WAVELETS OTHER GOUTTE C, 1999, NEUROIMAGE, V9, P298 HARTIGAN JA, 1979, APPL STATIST, V28, P100 HOEHN M, 2001, J MAGN RESON IMAGING, V14, P491 HOUSTON GC, 2001, MAGN RESON IMAGING, V19, P905 HYVARINEN A, 1999, IEEE T NEURAL NETWOR, V10, P626 IRELAND MD, 2003, 39017 SOC NEUR WASH KHERIF F, 2002, NEUROIMAGE, V16, P1068 KIVINIEMI V, 2003, NEUROIMAGE, V18, P253 LANGE N, 1999, STAT MED, V18, P2401 LONGA EZ, 1989, STROKE, V20, P84 LUO F, 2003, MAGNET RESON MED, V49, P264 LUO WL, 2003, NEUROIMAGE, V19, P1014 MALLAT S, 1998, WAVELET TOUR SIGNAL MANDEVILLE JB, 1998, MAGNET RESON MED, V39, P615 MAROTA JJA, 2000, NEUROIMAGE, V11, P13 MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P160 MOSER E, 1999, INT J IMAG SYST TECH, V10, P166 NETSIRI C, 2003, MAGN RESON IMAGING, V21, P1097 PERCIVAL DB, 2000, WAVELET METHODS TIME PETERSSON KM, 1999, PHILOS T ROY SOC B, V354, P1239 PREECE M, 2001, BRAIN RES, V916, P107 RABEHESKETH S, 1997, STAT METHODS MED RES, V6, P215 REESE T, 2000, NMR BIOMED, V13, P43 RIPLEY B, 1996, PATTERN RECOGNITION RITSCHEL WA, 1999, HDB BASIC PHARMACOKI ROSEN BR, 1990, MAGNET RESON MED, V14, P249 SOMORJAI RL, 2002, ARTIF INTELL MED, V25, P5 SUGAR CA, 2003, J AM STAT ASSOC, V98, P750 XI ZX, 2002, MAGNET RESON MED, V48, P838 N R P D P Y V L I S B P E P U T E R P T A U T I S O A B 46 JAN 15 2005 24 2 281 295 ISI:000226454500002 J Goelman, G Radial correlation contrast - A functional connectivity MRI contrast to ma communication NEUROIMAGE A functional connectivity MRI method that groups neigbboring voxels in relati cross-correlation between their time courses is presented. This grouping ge is assumed to provide insights into the local organization of neuronal act spatial resolution fMRI rat data subjected to electric forepaw sensory stim mu(1)) shows a significant localized increase of the vector field amplitude of the primary sensory cortex and in layer 2/3 of the primary motor cortex, su with local neuronal communication. Vector field phases exhibit a transition random-like orientations during rest to clusters of common orientations. Clus dependent on the radii of the vector field calculation, and shuffling voxe generates a random-like vector orientation instead. This suggests that chang activation represent changes in the internal correlation between voxels th in the internal neuronal communication. (C) 2004 Elsevier Inc. All rights C ARFANAKIS K, 2000, MAGN RESON IMAGING, V18, P921 R BAUNE A, 1999, NEUROIMAGE, V9, P477 BISWAL B, 1995, MAGNET RESON MED, V34, P537 BISWAL BB, 1997, NMR BIOMED, V10, P165 BISWAL BB, 1999, J COMPUT ASSIST TOMO, V23, P265 BUCHEL C, 1997, CEREB CORTEX, V7, P768 CORDES D, 2000, AM J NEURORADIOL, V21, P1636 CORDES D, 2001, AM J NEURORADIOL, V22, P1326 CORDES D, 2002, MAGN RESON IMAGING, V20, P305 EMILIO S, 2001, NAT REV NEUROSCI, V2, P539 FILZMOSER P, 1999, MAGN RESON IMAGING, V17, P817 FLETCHER P, 1999, NEUROIMAGE, V9, P337 FRISTON KJ, 1994, HUMAN BRAIN MAPPING, V1, P153 GOUTTE C, 1999, NEUROIMAGE, V9, P298 GRAY CM, 1999, NEURON, V24, P31 HEEGER DJ, 2000, NAT NEUROSCI, V3, P631 KRUGER G, 2001, MAGNET RESON MED, V46, P631 LI SJ, 2000, MAGNET RESON MED, V43, P45 LOGOTHETIS NK, 2001, NATURE, V412, P150 LOWE MJ, 1998, NEUROIMAGE, V7, P119 MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P160 SEIDEMANN E, 1998, NATURE, V394, P72 SHADLEN MN, 1999, NEURON, V24, P67 N R P D P Y V L I S B P E P U T E R P T A U T I S 23 DEC 2004 23 4 1432 1439 ISI:000226041800019 J Thirion, B Faugeras, O Feature characterization in fMRI data: the Information Bottleneck approach MEDICAL IMAGE ANALYSIS O A Clustering is a well-known technique for the analysis of Functional Magnetic R B whose main advantage is certainly flexibility: given a metric on the datas characteristics of the data. But intrinsic to this approach are also the p the quantization accuracy, and the number of clusters necessary to describ Bottleneck (IB) approach to vector quantization, proposed by Bialek and Ti difficulties: (1) it deals with an explicit trade-off between quantization a so during the clustering procedure and not post hoc: (3) it takes into acc distribution of the features within the feature space and not only their m principled through an information theoretic formulation, which is relevant paper, we present how to benefit from this method to analyze fMRI data. Our of voxels according to the magnitude of their responses to several experim quantization provides a consistent representation of the data, allowing fo comparison of datasets. (C) 2004 Elsevier B.V. All rights reserved. C BALSLEV D, 2002, HUM BRAIN MAPP, V15, P135 R BAUMGARTNER R, 1998, MAGN RESON IMAGING, V16, P115 BAUNE A, 1999, NEUROIMAGE, V9, P477 BEZDEK JC, 1981, PATTERN RECOGNITION BULLMORE E, 1996, MAGNET RESON MED, V35, P261 CHUANG KH, 1999, IEEE T MED IMAGING, V18, P1117 COVER TM, 1991, ELEMENTS INFORMATION FADILI MJ, 2000, HUM BRAIN MAPP, V10, P160 FADILI MJ, 2001, MED IMAGE ANAL, V5, P55 FLANDIN G, 2003, P 9 OHBM NEUR, V19 FRISON L, 1992, STAT MED, V11, P1685 FRISTON KJ, 1997, 5PM97 COURSE NOTES FRISTON KJ, 2000, NEUROIMAGE, V12, P196 GATH I, 1989, IEEE T PATTERN ANAL, V11, P773 GOUTTE C, 1999, NEUROIMAGE, V9, P298 GOUTTE C, 2001, HUM BRAIN MAPP, V13, P165 GUSTAFSON DE, 1979, P IEEE CDC SAN DIEG, P761 ICHIHASHI H, 2000, AFSS 2000 ICHIHASHI H, 2001, 10 IEEE INT C FUZZ S JARMASZ M, 2003, CONCEPTS MAGN RESO A, V16, P50 MOLLER U, 2002, NEUROIMAGE, V17, P431 PENNY W, 2003, IEEE T MED IMAGING, V22, P504 PEUSKENS H, 2001, J NEUROSCI, V21, P2451 STILL S, 2003, NIPS 2003 THIRION B, 2003, LECT NOTES COMPUTER, V2878, P831 TISHBY N, 1999, P 37 ANN ALL C COMM, P368 WISMULLER A, 2002, INT J COMPUT VISION, V46, P103 WOOLRICH MW, 2001, NEUROIMAGE, V14, P1370 N 28 R P DEC D P 2004 Y V L I S B P E P U T E R 8 4 403 419 ISI:000225930500001 P J T A Panzeri, S U Petroni, F Bracci, E T Exploring structure-function relationships in neocortical networks by means I S MEDICAL ENGINEERING & PHYSICS O A Determining the neuronal architecture underlying certain visual functions B for understanding how sensory processing is implemented in the brain. The physiological and biophysical data that is being currently acquired on the constrain its functional architecture. However, given the intrinsic complex it is difficult to provide a comprehensive framework to use these data in structure-function relationships. Here, we discuss the use of biophysically of neuronal networks, constructed to reflect the known properties of neoco modularity, as a tool to bring together anatomy and physiology. We illustr of the neuro-dynamics modelling approach by considering recent studies on functional structure of the visual cortex and its response timing, and on th of neuronal oscillations in the gamma frequency range. We also critically disc theory and experiments could help this approach to become directly relevan (C) 2004 IPEM. Published by Elsevier Ltd. All rights reserved. C BAIR W, 1999, CURR OPIN NEUROBIOL, V9, P447 R BATTAGLIA FP, 1998, NEURAL COMPUT, V10, P431 BRACCI E, 1999, J NEUROSCI, V19, P8104 BULLIER J, 1995, CURR OPIN NEUROBIOL, V5, P497 CALLAWAY EM, 1998, ANNU REV NEUROSCI, V21, P47 FELLEMAN DJ, 1991, CEREB CORTEX, V1, P1 FERRERA VP, 1992, NATURE, V358, P756 HILGETAG CC, 1996, SCIENCE, V271, P776 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 HUBEL DH, 1962, J PHYSIOL-LONDON, V160, P106 JEFFERYS JGR, 1996, TRENDS NEUROSCI, V19, P202 KOCH C, 1999, BIOPHYSICS COMPUTATI KOTTER R, 2000, PHILOS T ROY SOC B, V355, P127 LAMME VAF, 2000, TRENDS NEUROSCI, V23, P571 LIU SC, 2001, NEURAL NETWORKS, V14, P629 MARTIN KAC, 2002, CURR OPIN NEUROBIOL, V12, P418 NEALEY TA, 1994, J NEUROSCI, V14, P2069 ORAM MW, 1994, NEURAL NETWORKS, V7, P945 PANZERI S, 1999, NEURAL COMPUT, V11, P1553 PANZERI S, 2001, NETWORK-COMP NEURAL, V12, P423 PANZERI S, 2001, NEURON, V29, P769 PETRONI F, 2001, NEUROREPORT, V12, P2753 ROCKLAND KS, 1979, BRAIN RES, V179, P3 ROLLS ET, 1998, NEURAL NETOWRKS BRAI ROLLS ET, 1999, J COGNITIVE NEUROSCI, V11, P300 SCHENDAN HE, 2003, J COGNITIVE NEUROSCI, V15, P111 SCHMOLESKY MT, 1998, J NEUROPHYSIOL, V79, P3272 SHANNON CE, 1949, MATH THEORY INFORMAT SHARON D, 2002, SCIENCE, V295, P512 SINGER W, 2001, ANN NY ACAD SCI, V929, P123 SOMMER MA, 1998, J NEUROPHYSIOL, V80, P3331 STALEY KJ, 1995, SCIENCE, V269, P977 THORPE S, 1996, NATURE, V381, P520 THORPE SJ, 1989, CONNECTIONISM PERSPE, P63 TRAUB RD, 1985, NEUROSCIENCE, V14, P1033 TRAUB RD, 1996, J PHYSIOL-LONDON, V493, P471 TRAUB RD, 1996, NATURE, V383, P621 WHITTINGTON MA, 1995, NATURE, V373, P612 WHITTINGTON MA, 1997, J PHYSIOL-LONDON, V502, P591 WHITTINGTON MA, 2001, J NEUROSCI, V21, P1727 N R P D P Y V L I S B P E P U T E 40 NOV 2004 26 9 699 710 ISI:000225749600002 R P J T A Furber, SB U Bainbridge, WJ Cumpstey, JM Temple, S T Sparse distributed memory using N-of-M codes I S NEURAL NETWORKS O A An analysis is presented of a sparse distributed memory (SDM) inspired by that B P. (1988). Sparse distributed memory. Cambridge, MA: MIT Press] but modified t based on spiking neurons. The memory presented here employs sparse binary synaptic weights and a simple Hebbian learning rule. It is a two-layer net being similar to the `address decoder' in Jaeckel's [Jaeckel, L.A. (1989). A distributed memory. RIACS Technical Report 89.30, NASA Ames Research Centr Kanerva's SDM and the second (writeable) 'data store' layer being a correl proposed by Willshaw et al. [Willshaw, D. J., Buneman, O.P., & Longuet-Higgins associative memory. Nature, 222, 960-962]. The resulting network is shown to and is scalable. The analysis is supported by numerical simulations and gi configuration of the memory to be optinused for a range of noiseless and noisy e Ltd. All rights reserved. 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We detect character ( structural and functional motifs) in neuroanatomical data sets and ident motifs that occur in significantly increased numbers. Our analysis suggest networks maximize both the number and the diversity of functional motifs, whil motifs remains small. Using functional motif number as a cost function in obtain network topologies that resemble real brain networks across a broad spe including small-world attributes. These results are consistent with the hy neural architectures are organized to maximize functional repertoires and integration of information. 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Numerous clustering algorithms are available, and no one method application because an algorithm's performance depends on specific charact and fuzzy clustering are popular for neuroimaging analyses, and select hierar in the literature. It is unclear which clustering methods perform best for a simulation study, based on PET neuroimaging data, to evaluate the perfor algorithms, including a new procedure that builds on the kth nearest neighbor stopping rules that assist in determining the optimal number of clusters. algorithms perform best in our study, some of which are new to neuroimaging beta-flexible methods exhibiting the strongest performances. Furthermore, methods yield the best performances for noisy data, and the popular K-means an also perform reasonably well. The stopping rules also exhibit good performanc algorithms, and the pseudo-T-2 and pseudo-F stopping rules are superior fo simulations for both noisy and unscaled PET neuroimaging data, we recommend th or pseudo-T-2 stopping rule along with either Ward's or the beta-flexible c Wiley-Liss, Inc. 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However, the storage capacity of the entire networ this difficulty, we propose decreasing the connectivity while keeping the tot by introducing delayed synapses. In this paper, a discrete synchronous-type mo and their prunings is discussed as a concrete example of the proposal. Fir theory by employing statistical neurodynamics. This theory involves macrod dynamics of a network with serial delay elements. Next, considering the tr explained equations, we rederive macroscopic steady-state equations of the Fourier transformation. The storage capacities are analyzed quantitatively synaptic prunings are treated analytically: random pruning and systematic pr clear that in both prunings, the storage capacity increases as the length connectivity of the synapses decreases when the total number of synapses i interesting fact becomes clear: the storage capacity asymptotically approach In contrast, the storage capacity diverges in proportion to the logarithm of th pruning and the proportion constant is 4/pi. 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This review covers theoretical studies aimed at elucidat within hippocampal pyramidal cells. This processing involves both the intrin as well as the microcircuit of inhibitory interneurons that synapse onto t considered within the framework of associative memory function in areas CA hippocampus. 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Alth have been made easily available for online retrieval, the multiplicity of requirements of anatomical naming limit the intuitive access to the data. can be improved by observing a small set of conventions in data representa open up further opportunities for automated search and retrieval, for flexib for interoperability with other databases. 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In particular, we discuss how changes i network affect the spatiotemporal network dynamics qualitively. The three are, first, the local dynamics at the network nodes which includes fixed po chaotic dynamics; second, the presence of time delays via propagation along co the properties of the connectivity matrix such as its statistics, symmetry, Since the connection topology changes when anatomical scales are traversed network dynamics change. As a consequence different types of networks are en of neural organization. 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Visual perception natura of an integrated array of ongoing cognitive processes involving memory, pe and motor control. In many situations, these processes allow expectations t events. This article explores the idea that the formation of visual expect organization of visual cortical areas, providing a framework of contextual inf events are interpreted. Retinal inputs are treated as constraints that fee interacting visual cortical areas and thalamic nuclei, which are concurrentl another. Although the nature of expectational organization in the visual c a reasonable hypothesis is that expectation involves the mutual constraint patterns in multiple visual cortical areas. In this scenario, expectation activity patterns in high-level visual cortical areas that impose constrai on low-level areas according to the partial information that is available a One approach to testing this proposal is through the analysis of simultane potentials (LFPs) from local neuronal assemblies in multiple visual cortic multivariate autoregressive modeling is showing promise in revealing the o visual cortex. 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The orientation selectivity originates at least partly geniculate nuclei neurons with properly aligned receptive fields. In the pr the feedforward interactions between on- and off-pathways. Based on physio push-pull model with dominating opponent inhibition (DOI). We show that the m data on simple cells, such as contrast-invariant orientation tuning, sharpen increasing inhibition, and strong response decrements to stimuli with lumi identical parameter settings, we apply the model for the processing of syn We show that the model with DOI can robustly extract oriented contrast infor important, noise is adaptively suppressed, i.e. the model simple cells do not of different noise levels, while remaining sensitive to small contrast changes reveal a possible functional role of the strong inhibition as observed empi suppress responses to noisy input. (C) 2004 Elsevier Ltd. 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C ALDER R, 1981, GEOMETRY RANDOM FIEL R BAUMGARTNER R, 2000, NEUROIMAGE, V12, P240 BAUNE A, 1999, NEUROIMAGE, V9, P477 BECKMANN CF, 2000, NEUROIMAGE, V11, S614 BECKMANN CF, 2001, NEUROIMAGE S 2, V13, S75 BECKMANN CF, 2001, NEUROIMAGE S 2, V13, S76 BENJAMINI Y, 1995, J ROY STAT SOC B MET, V57, P289 BENJAMINI Y, 2001, ANN STAT, V29, P1165 BULLMORE E, 1996, MAGNET RESON MED, V35, P261 BULLMORE E, 2001, HUM BRAIN MAPP, V12, P61 BULLMORE ET, 1999, IEEE T MED IMAGING, V18, P32 DESCOMBES X, 1998, IEEE T MED IMAGING, V17, P1028 EVERITT BS, 1999, HUM BRAIN MAPP, V7, P1 FADILI M, 2000, HUM BRAIN MAPP, V10, P60 FORMAN SD, 1995, MAGNET RESON MED, V33, P636 FRISTON KJ, 1991, J CEREB BLOOD FLOW M, V11, P690 FRISTON KJ, 1993, J CEREBR BLOOD F MET, V13, P5 FRISTON KJ, 1994, HUMAN BRAIN MAPPING, V1, P214 FRISTON KJ, 1995, NEUROIMAGE, V2, P157 FRISTON KJ, 1995, NEUROIMAGE, V2, P166 FRISTON KJ, 1995, NEUROIMAGE, V2, P45 FRISTON KJ, 1996, HUM BRAIN MAPP, V4, P140 FRISTON KJ, 1996, NEUROIMAGE 1, V4, P223 FRISTON KJ, 2002, NEUROIMAGE, V16, P465 FRISTON KJ, 2003, NEUROIMAGE, P1240 GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721 GEMAN S, 1987, B INT STAT I, V52, P5 GENOVESE CR, 2001, THRESHOLDING STAT MA GOSSL C, 2000, MAGNET RESON MED, V43, P72 GOSSL C, 2000, P INT SOC MAGN RESON, V8, P816 GOSSL C, 2001, BIOMETRICS, V57, P554 GOUTTE C, 1999, NEUROIMAGE, V9, P298 GOUTTE C, 2001, HUM BRAIN MAPP, V13, P165 HARTVIG NV, 1999, STOCHASTIC GEOMETRY HARTVIG NV, 2000, HUM BRAIN MAPP, V11, P233 HAYASAKA S, 2003, NEUROIMAGE, V20, P2343 HOJENSORENSEN P, 2000, ADV NEURAL INF PROCE, V12 HOLMES AP, 1993, ANN NUCL MED, V7, S106 HOLMES AP, 1994, THESIS U GLASGOW HOLMES AP, 1996, J CEREBR BLOOD F MET, V16, P7 HOLMES AP, 1999, HBM1999 C JENKINSON M, 2001, FAST AUTOMATED N DIM JENKINSON M, 2002, NEUROIMAGE, V17, P825 KIEBEL SJ, 1999, NEUROIMAGE, V10, P756 KIMURA Y, 1999, NEUROIMAGE, V9, P554 LOCASCIO JJ, 1997, HUM BRAIN MAPP, V5, P168 MARCHINI J, 2002, R NEWS, V2, P17 MATTHEWS PM, 2001, FUNCTIONAL MAGNETIC MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P160 MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P368 NICHOLS T, 2003, STAT METHODS MED RES, V12, P419 POLINE JB, 1993, J CEREBR BLOOD F MET, V13, P425 POLINE JB, 1994, HUM BRAIN MAPP, V2, P103 POLINE JB, 1994, J CEREBR BLOOD F MET, V14, P639 POLINE JB, 1997, NEUROIMAGE, V5, P83 RAZ J, 1999, P SPIE C WAVELET APP, V8 RIPLEY BD, 1996, PATTERN RECOGNITION STOREY JD, 2002, J ROY STAT SOC B 3, V64, P479 STOREY SJ, 2003, ANN STAT, V31, P2013 TONONI G, 1998, NEUROIMAGE, V7, P133 TURKHEIMER FE, 1999, J CEREBR BLOOD F MET, V19, P1189 TURKHEIMER FE, 2001, NEUROIMAGE, V13, P920 WORLSEY KJ, 1992, J CEREB BLOOD FLOW M, V12, P900 WORSLEY KJ, 1994, ADV APPL PROBAB, V26, P13 WORSLEY KJ, 1995, ADV APPL PROBAB, V27, P943 WORSLEY KJ, 1995, NEUROIMAGE, V2, P173 WORSLEY KJ, 1996, HUM BRAIN MAPP, V4, P74 WORSLEY KJ, 1997, HUM BRAIN MAPP, V5, P254 WORSLEY KJ, 1997, NEUROIMAGE, V6, P305 WORSLEY KJ, 1999, HUM BRAIN MAPP, V8, P98 WORSLEY KJ, 2002, NEUROIMAGE, V15, P1 N R P D P Y V L I S B 71 JUL 2004 22 3 1203 P E 1213 P U ISI:000222423200018 T E R P T A U T I S O A B J Lauritzen, TZ Miller, KD The contributions of inhibition and noise to responses in V1 NEUROCOMPUTING We include a recently described class of inhibitory cells, complex cells unt as simple cells in a model of VI to study their contribution in shaping si complex cell inhibition can suffice to explain contrast-invariant orientatio frequency tuning of cortical simple cells. Given this complex cell inhibit inhibition from tuned simple inhibitory neurons acts to sharpen spatial freq stability of cortical activity. Intracortical inhibition is needed to achie voltage tuning, which is converted by physiological noise levels into cont (C) 2004 Elsevier B.V. All rights reserved. C ANDERSON JS, 2000, SCIENCE, V290, P1968 R DESTEXHE A, 2001, NEUROSCIENCE, V107, P13 FERSTER D, 2000, ANNU REV NEUROSCI, V23, P441 HIRSCH JA, 2000, SOC NEUR ABSTR, V26, P1083 HIRSCH JA, 2003, NAT NEUROSCI, V6, P1300 HUBEL DH, 1962, J PHYSIOL-LONDON, V160, P106 KRUKOWSKI AE, 2001, NAT NEUROSCI, V4, P424 LAMPL I, 1999, NEURON, V22, P361 LAURITZEN TZ, 2003, J NEUROSCI, V23, P10201 PALMER S, 2002, EFFECTS INHIBITORY C SKOTTUN BC, 1987, J NEUROPHYSIOL, V57, P773 TROYER TW, 1998, J NEUROSCI, V18, P5908 UHLENBECK GE, 1930, PHYS REV, V36, P823 N 13 R P JUN D P 2004 Y V 58-60 L B 901 P E 907 P U ISI:000222245900130 T E R P J T A Fregnac, Y U Monier, C Chavane, F Baudot, P Graham, L T Shunting inhibition, a silent step in visual cortical computation I S JOURNAL OF PHYSIOLOGY-PARIS O A Brain computation, in the early visual system, is often considered as a hi B features extracted ill a given sensory relay are not present in previous s particular, orientation preference and its fine tuning selectivity are fun most cortical cells and they are not observed at the preceding geniculate identifying the mechanisms and circuitry underlying these computations. Seve have been proposed, giving different weights to the feedforward thalamocorti recurrent architectures. Within this context, an important issue is whethe fundamental for the genesis of stimulus selectivity, or rather normalizes spik to other features such as stimulus strength or contrast, without influenci preference expressed in the excitatory input alone. We review here experim the presence or absence of inhibitory input evoked by non-preferred orientat current clamp and voltage clamp recordings are analyzed in the light of ne increase the visibility of inhibitory input, and (2) to continuously measure of input conductances. We conclude that there exists a diversity of synaptic the same profile of spike-based orientation selectivity, and that this div anatomical non-homogeneities in input sampling provided by the local contex intracortical network in which the considered cortical cell is embedded. ( Ltd. 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Both performance coefficients (PCs) clearly show that t algorithm perform significantly better than all the other methods using ou methods the ward linkage algorithm performs best under our simulation desi gas method seems to be the best choice for fMRI cluster analysis, given it activated pixels (true positives (TPs)) whilst minimizing the misclassific (false positives (FPs)), and in the stability of the results achieved. (C) reserved. 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The functional role of chandelier cells i preferential loss of this cell type at epileptic loci suggests a role in e examination of whisker-and spontaneous activity-evoked responses in chandelie nonpyramidal neurons and regular-spiking pyramidal neurons in layer 2/3 of th nonpyramidal neurons, including chandelier cells, basket cells, neurogliafor net basket cells, bitufted cells, and regular-spiking pyramidal neurons al multiple whiskers on the contralateral face. Whisker stimulation, however, preceded by an earlier IPSP and no action potentials in chandelier cells, diff and pyramidal neurons. In addition, chandelier cells display a larger rece than other fast-spiking nonpyramidal neurons and pyramidal neurons. Notably, in vivo recordings show that chandelier cells, which rarely fire action po more robustly than other types of cortical neurons when the overall cortica chandelier cells may not process fast ascending sensory information but inst excessive excitatory activity in neuronal networks. 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Well water samples from the Madison strontium isotopic ratios that match their carbonate host rocks. Casper Aq Basin) have strontium isotopic ratios that differ from the bulk host rock; Casper Sandstone indicates that most of the strontium in Casper Aquifer gr preferential dissolution of carbonate cement. Strontium isotope data from bo along with dye tracing experiments in the Bighorn Basin and tritium data fr that waters in carbonate or carbonate-cemented aquifers acquire their stron quickly-on the order of decades. Strontium isotopes were also used successfully to verify previously identifie waters in the Laramie Basin. The strontium isotopic compositions of ground wat also suggest previously unrecognized mixing between Casper and Precambrian demonstrate the utility of strontium isotopic ratio data in identifying gro interactions. 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However, error-free retrieval states of such oscillato unstable, resulting in a near zero capacity. This puts the networks at disa classical Hopfield network. Here we propose a simple remedy for this undesirabl that the error-free capacity of our oscillatory, associative-memory network of the Hopfield network. 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The steps of the work accomplis shown that the detection performance evaluated by the area under the recei curve is directly related to the signal-to-noise ratio (SNR) of the compos detection process. 2) Detection and segmentation of activated areas are fo framework. In this formulation, a linear transformation (image combination me to maximize the SNR of the activated areas subject to the constraint of re analytical solution for the problem is found. 4) Image pixel vectors and e (signature) for inactive pixels are used to calculate weighting vector and 5) Signatures of the activated regions are used to segment different activ the proposed method are compared with those generated by the conventional metho and z statistic). Detection performance and SNRs of the images are compare outperforms the conventional methods of fMRI analysis. In addition, it is m require a priori knowledge of the fMRI response to the paradigm. Since the the work is done analytically, numerical implementation and execution of th the conventional methods. 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We assume that the model consists of excitatory neuron the neurons are connected to each other. The excitatory neurons are distin selective neurons and one group of non-selective neurons. The selective ne subpopulations in which each selective neuron belongs to only one of subpo neurons are assumed not to form any subpopulation. Synaptic strengths between are assumed to be potentiated. By the numerical simulations, persistent firing emerges; the persistent firing corresponds to the retention of memory as on memory. We find that the strength of external input and the strength of N-m important factors for dynamical behaviors of the network; for example, if w external input to a subpopulation while the persistent firing is occurring persistent firing occurs in the subpopulation or is sustained against the reveal that the neural network as for the function of the working memory is con and the external stimuli within the proposed model. We also find that the the selective neurons shows a kind of phase transition as a function of th synapses, (C) 2003 Elsevier B.V. All rights reserved. C AMIT DJ, 2003, CEREB CORTEX, V13, P435 R BEAR MF, 1996, NEUROSCIENCE EXPLORI BRODY CD, 2003, CURR OPIN NEUROBIOL, V13, P204 BRUNEL N, 2001, J COMPUT NEUROSCI, V11, P63 CASADO JM, 2003, PHYS LETT A, V310, P400 COMPTE A, 2000, CEREB CORTEX, V10, P1047 DESTEXHE A, 1994, NEURAL COMPUT, V6, P14 DURSTEWITZ D, 1999, J NEUROSCI, V19, P2807 DURSTEWITZ D, 2000, J NEUROPHYSIOL, V83, P1733 DURSTEWITZ D, 2000, NAT NEUROSCI, V3, P1184 FUNAHASHI S, 1989, J NEUROPHYSIOL, V61, P331 GOLDMANRAKIC PS, 1996, P NATL ACAD SCI USA, V93, P13473 HODGKIN AL, 1952, J PHYSIOL, V117, P500 JENSEN O, 2002, NEUROIMAGE, V15, P575 KIM S, 2000, PHYSICA A, V288, P380 KNOBLAUCH A, 2002, NEUROCOMPUTING, V44, P19 KRAUSE JB, 2000, NEURAL NETWORKS, V13, P847 KWON O, 2002, PHYS LETT A, V298, P319 LISMAN JE, 1998, NAT NEUROSCI, V1, P273 MIYASHITA S, 2003, NEUROCOMPUTING, V52, P327 RANGANATH C, 2003, NEUROPSYCHOLOGIA, V41, P378 READ HL, 1996, NEUROSCIENCE, V75, P301 RENART A, 2003, NEURON, V38, P473 TEGNER J, 2002, BIOL CYBERN, V5, P471 WANG XJ, 1996, J NEUROSCI, V16, P6402 N 25 R P MAR 15 D P 2004 Y V 334 L I S B P E P U T E R 3-4 600 614 ISI:000189094800019 P S T A Aligue, FJL U Sotoca, IA Troncoso, IA Orellana, CJG Velasco, HG T A neural associative pattern classifier I S ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2002, PROCEEDINGS O S LECTURE NOTES IN ARTIFICIAL INTELLIGENCE E A In this work, we study the behaviour of the Bidirectional Associative Memory ( B neural structure, with a view to its possible improvements as a useful Patter associations from unknown inputs, once mentioned classes have been previous prototypes. The best results have been obtained by suitably choosing the t thresholds, and the activation functions of the network's neurones, by mean described in the paper. In order to put forward the advantages of these pro has been applied on an especially popular hand-written character set as the database, and with one of the UCI's data bases. In all cases, the method l the performance achievable by a BAM, with a 0% error rate. 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The Information Bottleneck (IB) ap [11] addresses these difficulties: 1) it deals with an explicit tradeoff b fidelity; 2) it does so during the clustering procedure. and not post hoc; statistical distribution of the features within the feature space and not last, it is principled through an information theoretic formulation, which i In this paper, we present how to benefit from this method to analyze fMRI clustering of voxels according to the magnitude of their responses to seve The IB quantization provides a consistent representation of the data, allowi C BALSLEV D, 2002, HUM BRAIN MAPP, V15, P135 R BAUMGARTNER R, 1998, MAGN RESON IMAGING, V16, P115 BAUNE A, 1999, NEUROIMAGE, V9, P477 FADILI MJ, 2000, HUM BRAIN MAPP, V10, P160 FADILI MJ, 2001, MED IMAGE ANAL, V5, P55 FRISTON KJ, 1997, 97 SPM GOUTTE C, 1999, NEUROIMAGE, V9, P298 GOUTTE C, 2001, HUM BRAIN MAPP, V13, P165 MOLLER U, 2002, NEUROIMAGE, V17, P431 PEUSKENS H, 2001, J NEUROSCI, V21, P2451 TISHBY N, 1999, P 37 ANN ALL C COMM, P368 WISMULLER A, 2002, INT J COMPUT VISION, V46, P103 N 12 R P 2003 Y V 2879 L B 83 P E 91 P U ISI:000188180400011 T E R P J T A Chen, S U Bouman, CA Lowe, MJ T Clustered components analysis for functional MRI I S IEEE TRANSACTIONS ON MEDICAL IMAGING O A A common method of increasing hemodynamic response (SNR) in functional magn B is to average signal timecourses across voxels. This technique is potentia hemodynamic response may vary across the brain. Such averaging may destroy temporal evolution of the fMRI response that stem from either differences i tissue or actual differences in the neural response between two averaged vox presented in this paper in order to aid in an improved SNR estimate of the preserving statistically significant voxel-wise differences. The first tec estimation for periodic stimulus paradigms that involves a simple thresholdi via dimensionality reduction. The second technique that we call clustered c amplitude-independent clustering method based upon an explicit statistical unsupervised method for estimating the number of clusters. Our methods are verification and comparison to other techniques. A human experiment was also d functional cortices. Our methods separated hemodynamic response signals in classified according to tissue characteristics. 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We use connectivity data on larg demonstrate the virtues of this approach and highlight some interesting feat up in previously published material. Some implications of our approach for def relevant to functional segregation and functional integration, for example, f are discussed. (C) 2003 Elsevier Ltd. All rights reserved. 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Work has already begun on studying a host of spontaneous EEG pheno and sleep patterns to epileptiform discharges and seizures, with far reach However, the transformation of EEG data into linear models suitable for voxel testing is central to the endeavour. This in turn is predicated upon a num the manner in which the generators of EEG phenomena may engender changes in th (BOLD) signal. Furthermore, important limitations are posed by a set of co 'paradigmless fMRI'. Here, these issues are assembled and explored to provid and unresolved questions, with an emphasis on applications in epilepsy. (C) reserved. 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1989, BRAIN TOPOGRAPHY, V2, P31 WONG PKH, 1991, BRAIN TOPOGR, V4, P105 ZARAHN E, 2001, NEUROIMAGE, V14, P768 N R P D P Y V L I S B P E P U T E R P T A U T I S O S E A B 213 SEP 2003 43 1 110 133 ISI:000186317100008 S Knoblauch, A Optimal matrix compression yields storage capacity 1 for binary Willshaw a ARTIFICAIL NEURAL NETWORKS AND NEURAL INFORMATION PROCESSING - ICAN/ICONIP LECTURE NOTES IN COMPUTER SCIENCE The classical binary Willshaw model of associative memory has an asymptoti approximate to 0.7 which exceeds the capacities of other (e.g., Hopfield-l its practical use is severely limited, since the asymptotic capacity is reach n of neurons and for sparse patterns where the number kappa of one-entries value kappa(opt) (n) (typically kappa(opt) = log n). In this work I demonst of the binary memory matrix by a Huffman or Golomb code can increase the a 1. Moreover, it turns out that this happens for a very broad range of kappa bei kappa constant) or moderately-sparse (e.g., kappa = rootn). A storage capa already achieved for practical numbers of neurons. C BENTZ HJ, 1989, NEURAL NETWORKS, V2, P289 R COVER TM, 1991, ELEMENTS INFORMATION GOLOMB S, 1966, IEEE T INFORM THEORY, V12, P399 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 HOPFIELD JJ, 1986, SCIENCE, V233, P625 HUFFMAN DA, 1952, P IRE, V40, P1098 KNOBLAUCH A, UNPUB WILLSHAW ASS M KNOBLAUCH A, 2001, NEURAL NETWORKS, V14, P763 KNOBLAUCH A, 2002, BIOL CYBERN, V87, P168 KOSKO B, 1988, IEEE T SYST MAN CYB, V18, P49 PALM G, 1980, BIOL CYBERN, V36, P19 PALM G, 1991, CONCEPTS NEUROSCI, V2, P97 RACHKOVSKIJ DA, 2001, NEURAL COMPUT, V13, P411 SCHWENKER F, 1996, NEURAL NETWORKS, V9, P445 SOMMER FT, 1999, NEURAL NETWORKS, V12, P281 STEINBUCH K, 1961, KYBERNETIK, V1, P36 WILLSHAW DJ, 1969, NATURE, V222, P960 N 17 R P 2003 Y V 2714 L B 325 P E 332 P U ISI:000185378100039 T E R P T A U T I S O A B J Raffone, A van Leeuwen, C Dynamic synchronization and chaos in an associative neural network with mu CHAOS Associative memory dynamics in neural networks are generally based on attr fixed-point attractors works if only one memory pattern is retrieved at th simultaneous retrieval of more than one pattern. Stable phase-locking of p cycle attractors leads to incorrect feature bindings if the simultaneously of their features. We investigate retrieval dynamics of multiple active pat model neurons. Several memory patterns are kept simultaneously active and a dynamic itinerant synchronization between neurons. Neurons representing sh synchronization between patterns, thus multiplexing their binding relation mechanism for self-organized readout or decoding of memory pattern coheren potentiation and short-term depression of synaptic weights. (C) 2003 Ameri C ABELES M, 1991, CORTICONICS NEURAL C R AMIT DJ, 1989, MODELING BRAIN FUNCT AMIT DJ, 1995, BEHAV BRAIN SCI, V18, P617 ANDERSON JR, 1990, COGNITIVE PSYCHOL IT ARBIB MA, 1998, NEURAL ORG STRUCTURE BADDELEY A, 1992, SCIENCE, V255, P556 BAHRICK HP, 1984, J EXP PSYCHOL GEN, V113, P1 BECHTEL W, 2002, CONNECTIONISM MIND P CASTELOBRANCO M, 2000, NATURE, V405, P685 CAVE KR, 1999, PSYCHON B REV, V6, P204 COWAN N, 2001, BEHAV BRAIN SCI, V24, P87 DAUCE E, 2002, BIOL CYBERN, V887, P315 DIESMANN M, 1999, NATURE, V402, P529 ECKHORN R, 1988, BIOL CYBERN, V60, P121 ECKHORN R, 1992, INFORMATION PROCESSI, P385 ENGEL AK, 1992, TRENDS NEUROSCI, V15, P218 FELLEMAN DJ, 1991, CEREB CORTEX, V1, P1 FODOR JA, 1988, COGNITION, V28, P3 FUJII H, 1996, NEURAL NETWORKS, V9, P1303 GEORGOPOULOS AP, 1989, SCIENCE, V243, P234 GILDEN DL, 2001, PSYCHOL REV, V108, P33 GONG P, 2003, NEUROSCI LETT, V336, P33 GRAY CM, 1989, NATURE, V338, P334 GRAY CM, 1999, NEURON, V24, P31 HANSEL D, 1992, PHYS REV LETT, V68, P718 HINDMARSH JL, 1984, P ROY SOC LOND B BIO, V221, P87 HOPFIELD J, 1982, P NATL ACAD SCI USA, V9, P2554 HORN D, 1991, NEURAL COMPUT, V3, P31 KAASPETERSEN C, 1987, CHAOS BIOL SYSTEMS, P183 KANEKO K, 1990, PHYSICA D, V41, P137 KANEKO K, 2002, PHYS REV E, V66 KONIG P, 1991, NEURAL COMPUT, V3, P155 LI Z, 1989, BIOL CYBERN, V61, P379 LISMAN JE, 1995, SCIENCE, V267, P1512 LUCK SJ, 1997, NATURE, V390, P279 MILNOR J, 1985, COMMUN MATH PHYS, V99, P177 MURRE JMJ, 1992, NEURAL NETWORKS, V5, P55 RAFFONE A, 2001, CONNECT SCI, V13, P349 RAFFONE A, 2001, J COGNITIVE NEUROSCI, V13, P766 SHADLEN MN, 1999, NEURON, V24, P67 SHARKEY AJC, 2002, HDB BRAIN THEORY NEU SKARDA CA, 1987, BEHAV BRAIN SCI, V10, P161 SOMMER FT, 2001, NEURAL NETWORKS, V14, P825 SPORNS O, 1989, P NATL ACAD SCI USA, V86, P7265 TSUDA I, 1984, PROG THEOR PHYS SUPP, V79, P241 TSUDA I, 1992, NEURAL NETWORKS, V5, P313 TSUDA I, 1993, BEHAV BRAIN SCI, V16, P475 TSUDA I, 2001, BEHAV BRAIN SCI, V24, P793 VAADIA E, 1995, NATURE, V373, P515 VANLEEUWEN C, 1997, J MATH PSYCHOL, V41, P319 VANLEEUWEN C, 2001, COGNITIVE PROCESSING, V2, P67 VONDERMALSBURG C, 1981, 812 MAX PLANCK I BIO VONDERMALSBURG C, 1986, BIOL CYBERN, V54, P29 VONDERMALSBURG C, 1999, NEURON, V24, P95 WANG D, 1990, NEUR COMP, V2, P94 WANG DL, 1997, NEURAL COMPUT, V9, P805 N R P D P Y V L I S B P E P U T E R 56 SEP 2003 13 3 1090 1104 ISI:000184992600033 P J T A Lansner, A U Fransen, E Sandberg, A T Cell assembly dynamics in detailed and abstract attractor models of cortic I S THEORY IN BIOSCIENCES O A During the last few decades we have seen a convergence among ideas and hyp B principles underlying human memory. Hebb's now more than fifty years old c plasticity and cell assemblies, formalized mathematically as attractor neura the most viable and productive theoretical frameworks. It suggests plausib aspects of active memory like perceptual completion, reconstruction and ri We review the biological plausibility of these theories and discuss some cri associative memory functionality in the light of simulation studies of mod properties. The focus is on memory properties and dynamics of networks mod minicolumns and hypercolumns. Biophysical compartmental models demonstrate a cell assembly operations with fast convergence and low firing rates. Using reasonable relative connection densities and amplitudes. An abstract attra systems level psychological phenomena seen in human memory experiments as t effects. We conclude that there is today considerable substance in Hebb's theory of cel network formulations, and that they have contributed to increasing our underst memory function. The criticism raised with regard to biological and psychological plausibil capacity, slow retrieval etc has largely been disproved. Rather, this paradi from new experimental data as well as computational modeling. 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The peripheral area P is modeled cortex, while the central area C is modeled as an associative memory represent to Hebbian learning. Without feedback from area C, spikes corresponding to P are synchronized only locally (slow state). Feedback from C can induce fast of synchronization ranges (fast state). Presenting a super-position of sev segmentation happens on a time scale of hundreds of milliseconds by altern fast state, where neurons representing the same object are simultaneously our simulation results to various phenomena observed in neurophysiological stimulus-dependent synchronization of fast oscillations, synchronization on activity, and attention-dependent neural activity. 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This mechan on the dynamics of the neural units, can operate at the single neuron leve level of neuronal ensembles as well. Due to the repeated occurrence of sequen the sequence-sensitive elements become more firmly established and, by sub strings, a process called auto-associative substitution learning (AASL) is neuronal counterparts of the string elements involved in the substitution pro thereby providing a brain basis of what can be described linguistically as of grammar. A network of sequence detectors may constitute grammar circuits a separate set of mechanisms establishing temporary binding and recursion C ABELES M, 1993, J NEUROPHYSIOL, V70, P1629 R BARLOW HB, 1964, J PHYSIOL-LONDON, V173, P377 BARLOW HB, 1965, J PHYSL, V178, P477 BIENENSTOCK E, 1996, J PHYSIOLOGY-PARIS, V90, P251 BLOOMFIELD L, 1933, LANGUAGE BRAITENBERG V, 1978, LECTURE NOTES BIOMAT, V21, P171 BRAITENBERG V, 1997, BEHAV BRAIN SCI, V20, P229 BRAITENBERG V, 1998, CORTEX STAT GEOMETRY BRAITENBERG V, 2001, J COMPUT NEUROSCI, V10, P71 CHOMSKY N, 1963, HDB MATH PSYCHOL, V2, P323 CRESPIREGHIZZI S, 2001, COMPUT LANG, V27, P105 EGELHAAF M, 1989, J OPT SOC AM A, V6, P1070 FUSTER JM, 1995, MEMORY CEREBRAL CORT FUSTER JM, 1997, TRENDS NEUROSCI, V20, P451 HARE M, 1995, LANG COGNITIVE PROC, V10, P601 HARRIS ZS, 1951, STRUCTURAL LINGUISTI HAUSER MD, 2002, SCIENCE, V298, P1569 HECK D, 1993, NEUROSCI LETT, V157, P95 HECK D, 2002, HUM MOVEMENT SCI, V21, P411 HUBEL D, 1995, EYE BRAIN VISION JOSHI AK, 1990, LANG COGNITIVE PROC, V5, P1 KLEENE SC, 1956, AUTOMATA STUDIES, P3 MCCULLOCH WS, 1943, B MATH BIOPHYS, V5, P115 MILNER PM, 1957, PSYCHOL REV, V64, P242 MILNER PM, 1999, AUTONOMOUS BRAIN PAGE MPA, 1998, PSYCHOL REV, V105, P761 PALM G, 1980, BIOL CYBERN, V36, P19 PALM G, 1982, NEURAL ASSEMBLIES PALM G, 1993, BRAIN THEORY SPATIOT, P261 PINKER S, 2002, TRENDS COGN SCI, V6, P472 PRUT Y, 1998, J NEUROPHYSIOL, V79, P2857 PULVERMILLER F, 2003, NEUROSCIENCE LANGUAG PULVERMULLER F, 1993, BRAIN THEORY SPATIOT, P131 PULVERMULLER F, 1998, NETWORK-COMP NEURAL, V9, R1 PULVERMULLER F, 1999, BEHAV BRAIN SCI, V22, P253 PULVERMULLER F, 2002, PROG NEUROBIOL, V67, P85 REICHARDT W, 1959, Z NATURFORSCH B, V14, P674 RUMELHART DE, 1987, MECH LANGUAGE ACQUIS SAKURAI Y, 1999, NEUROSCI BIOBEHAV R, V23, P785 SOMMER FT, 2001, NEURAL NETWORKS, V14, P825 VARJU D, 1967, Z NATURFORSCH B, V22, P1343 WILLSHAW DJ, 1969, NATURE, V222, P960 WILLSHAW DJ, 1972, P ROY SOC LOND B BIO, V182, P233 YOUNG MP, 1995, ANAL CORTICAL CONNEC ZIPSER D, 1993, J NEUROSCI, V13, P3406 N 45 R P MAY D P Y V L I S B P E P U T E R P T A U T I S O A B 2003 122 1 87 103 ISI:000183327000007 J Baudelet, C Gallez, B Cluster analysis of BOLD fMRI time series in tumors to study the heterogen to treatment MAGNETIC RESONANCE IN MEDICINE BOLD-contrast functional MRI (fMRI) has been used to assess the evolution o flow after treatment. The aim of this study was to evaluate K-means-based clus data-driven method. The advantage of this approach is that it can be used t the need for prior knowledge concerning the hemodynamic response function. to illustrate different types of BOLD fMRI response inside tumors: the firs challenge with carbogen, and the second after pharmacological modulation o flunarizine. To improve the efficiency of the clustering, a power density sp to isolate voxels for which signal changes did not originate from noise or presented here can be used to assess hemodynamic response to treatment, an of the tumor with heterogeneous responses. (C) 2003 Wiley-Liss, Inc. 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Recently, we proposed attractors' where attractors embedded in a recurrent network via Hebbian learn response to external input rather than becoming manifest themselves. This a context-sensitive internal codes in complex situations. Latent attractors explaining computations within the hippocampus-a brain region of fundamenta spatial learning. Latent attractor networks are a special case of associative memory networks. T of a two-layer recurrent network with attractors stored in the recurrent conne learning rule. The firing in both layers is competitive-K winners take all allowed to fire, K, is smaller than the size of the active set of the store of latent attractor networks depends on the number of such attractors that In this paper, we use signal-to-noise methods developed for standard assoc a theoretical and computational analysis of the capacity and dynamics of la is an important first step in making latent attractors a viable tool in the rep The method developed here leads to numerical estimates of capacity limits an networks. The technique represents a general approach to analyse standard ass competitive firing. The theoretical analysis is based on estimates of the dendritic sum distri approximation. Because of the competitive firing property, the capacity re numerically by iteratively computing the probability of erroneous firings. Th the simple case analysis which accounts for the correlations between weigh the detailed case analysis which includes also the temporal correlations b and previous state. The latter case predicts better the dynamics of the netwo spurious firing. The theoretical analysis also shows the influence of the on the storage capacity. 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The update of the individual pattern-bits is controlled by an excita its arguement the inner product between the stored memory patterns and the inp is to analyze the dynamics of pattern recall when the input patterns are corr unrestricted class. We make three contributions. First, we show how to ide which maximizes the separation (the Fisher discriminant) between the uncorru input pattern and the remaining patterns residing in the memory. Moreover, function which gives maximum separation is exponential when the input bitdistribution. Our second contribution is to develop an expression for the e probability on the input pattern after one iteration. We show how to identify minimizes the bit-error probability. However, there is no closed-form soluti must be recovered numerically. The relationship between the excitation fun two different approaches is examined for a binomial distribution of bit-er is to develop a semiempirical approach to the modeling of the dynamics of th a numerical means of predicting the recall error rate of the memory. It also all for the storage capacity for a given recall error rate. 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Relevant problems include movement recogniti tracking, body correspondence, coordinate transformation from external to observed against previously learned movement, resolution of redundant degr unconstrained by the observation, suitable movement representations for imit control, etc. All of these topics by themselves are active research proble neurobiological sciences, such that their combination into a complete imitat undertaking-indeed, one could argue that we need to understand the complet a strategy to untangle the complexity of imitation, this paper will examin computational point of view, i.e. we will review statistical and mathemati suggested for tackling parts of the imitation problem, and discuss their m underlying principles. Given the focus on action recognition of other contri this paper will primarily emphasize the motor side of imitation, assuming already identified important features of a demonstrated movement and create information. Based on the formalization of motor control in terms of control performance criteria, useful taxonomies of imitation learning can be gener approaches and future research directions. 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In our studi memorization task during depth EEG recording. Afterwards, the difference bet (ERPs) corresponding to subsequently remembered versus forgotten words was a revealed that successful memory encoding is characterized by an early process within 300 ms following stimulus onset. This rhinal process precedes a hipp about 200 ms later. Further investigation revealed that the rhinal process seem preprocessing which supports memory formation, whereas the hippocampal proc of an exclusively mnemonic operation. These studies yielded only indirect e rhinal cortex and hippocampus. Direct evidence for a memory related coopera however, has been found in a study analyzing so called gamma activity, EEG This investigation showed that successful as opposed to unsuccessful memor an initial enhancement of rhinal-hippocampal phase synchronization, which desynchronization. Present knowledge about the function of phase synchronize this phase coupling and decoupling initiates and later terminates communic structures. Phase synchronized rhinal-hippocampal gamma activity may, more synaptic modifications and thus provide an initial step of declarative mem level. C ABBOTT LF, 2000, NAT NEUROSCI S, V3, P1178 R AMARAL DG, 1989, NEUROSCIENCE, V31, P571 AMARAL DG, 1990, HUMAN NERVOUS SYSTEM, P711 BECK H, 2000, J NEUROSCI, V20, P7080 BRESSLER SL, 1987, BRAIN RES, V409, P285 BREWER JB, 1998, SCIENCE, V281, P1185 BROWN MW, 2001, NAT REV NEUROSCI, V2, P51 BULLOCK TH, 1995, ELECTROEN CLIN NEURO, V95, P161 BUZSAKI G, 1996, CEREB CORTEX, V6, P81 COFER CN, 1952, AM J PSYCHOL, V65, P75 CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671 DESMEDT JE, 1994, NEUROSCI LETT, V168, P126 DUZEL E, 2001, P NATL ACAD SCI USA, V98, P8101 ECKHORN R, 1988, BIOL CYBERN, V60, P121 EICHENBAUM H, 2000, NAT REV NEUROSCI, V1, P41 ENGEL AK, 1991, P NATL ACAD SCI USA, V88, P6048 ENGEL AK, 1991, SCIENCE, V252, P1177 ENGEL AK, 1999, CONSCIOUS COGN, V8, P128 ENGEL AK, 2001, TRENDS COGN SCI, V5, P16 FELL J, 2001, NAT NEUROSCI, V4, P1259 FERNANDEZ G, 1998, J NEUROSCI, V18, P1841 FERNANDEZ G, 1999, SCIENCE, V285, P1582 FERNANDEZ G, 2001, HUM BRAIN MAPP, V14, P251 FERNANDEZ G, 2002, HIPPOCAMPUS, V12, P514 FERNANDEZ G, 2002, TRENDS NEUROSCI, V25, P281 FREEMAN WJ, 1978, ELECTROENCEPHALOGR C, V44, P586 FRIEN A, 1994, NEUROREPORT, V5, P2273 FRIES P, 1997, P NATL ACAD SCI USA, V94, P12699 FRIES P, 2001, SCIENCE, V291, P1506 FRIES P, 2002, J NEUROSCI, V22, P3739 FUJITA I, 1992, NATURE, V360, P343 GABRIELI JDE, 1998, ANNU REV PSYCHOL, V49, P87 GOODMAN CS, 1993, CELL, V72, P77 GORDON B, 1983, J VERB LEARN VERB BE, V22, P24 GRAY CM, 1989, NATURE, V338, P334 GREGG V, 1976, RECALL RECOGNITION, P183 GRUBER T, 1999, CLIN NEUROPHYSIOL, V110, P2074 GRUNWALD T, 1998, P NATL ACAD SCI USA, V95, P3193 GRUNWALD T, 1999, P NATL ACAD SCI USA, V96, P12085 HALGREN E, 1980, SCIENCE, V210, P803 HEBB DO, 1949, ORG BEHAV HERRMANN CS, 2000, INT J PSYCHOPHYSIOL, V38, P265 HERRMANN CS, 2001, VIS COGN, V8, P593 HIRAI N, 1999, NEUROSCIENCE, V90, P1149 HUANG YY, 1996, LEARN MEMORY, V3, P74 KLEE M, 1977, J NEUROPHYSIOL, V40, P647 KREIMAN G, 2000, NATURE, V408, P357 KREITER AK, 1996, J NEUROSCI, V16, P2381 LABERGE D, 1997, CONSCIOUS COGN, V6, P149 LEBEDEV MA, 1995, J COMPUT NEUROSCI, V2, P313 LEOPOLD DA, 1999, TRENDS COGN SCI, V3, P254 LUTZENBERGER W, 1995, NEUROSCI LETT, V183, P39 MAGEE JC, 1997, SCIENCE, V275, P209 MARDIA KV, 1972, PROBABILITY MATH STA MARKRAM H, 1997, SCIENCE, V275, P213 MCCARTHY G, 1995, J NEUROSCI, V15, P1080 MENON V, 1996, ELECTROEN CLIN NEURO, V98, P89 MILTNER WHR, 1999, NATURE, V397, P434 MISHKIN M, 1997, PHILOS T ROY SOC B, V352, P1461 MULLER MM, 1996, EXP BRAIN RES, V112, P96 MULLER MM, 2000, INT J PSYCHOPHYSIOL, V38, P283 NOBLE CE, 1963, VERBAL BEHAV LEARNIN, P76 NOBRE AC, 1995, J NEUROSCI, V15, P1090 OTTEN LJ, 2001, BRAIN 2, V124, P399 PALLER KA, 1990, J EXP PSYCHOL LEARN, V16, P1021 RODRIGUEZ E, 1999, NATURE, V397, P430 RUBIN DC, 1986, MEM COGNITION, V14, P79 SANQUIST TF, 1980, PSYCHOPHYSIOLOGY, V17, P568 SCOVILLE WB, 1957, J NEUROL NEUROSUR PS, V20, P11 SMITH ME, 1986, ELECTROEN CLIN NEURO, V63, P145 SOMMER FT, 2001, NEURAL NETWORKS, V14, P825 STEINMETZ PN, 2000, NATURE, V404, P187 STOPFER M, 1997, NATURE, V390, P70 TALLONBAUDRY C, 1999, TRENDS COGN SCI, V3, P151 TIITINEN H, 1993, NATURE, V364, P59 VANROOST D, 1998, NEUROSURGERY, V43, P819 VARELA F, 2001, NAT REV NEUROSCI, V2, P229 VONDERMALSBURG C, 1981, 812 MPI BIOPH CHEM VONDERMALSBURG C, 1986, BIOL CYBERN, V54, P29 VONDERMALSBURG C, 1999, NEURON, V24, P95 WAGNER AD, 1998, SCIENCE, V281, P1188 WITTER MP, 1989, PROG NEUROBIOL, V33, P161 YOUNG MP, 1992, SCIENCE, V256, P1327 N R P Y V L I S B P E P U T E R P T A U T I S O A B 83 2002 13 4 299 312 ISI:000180514400002 J Fellenz, WA Taylor, JG The hidden-layer model of hippocampus NEUROCOMPUTING We analyze the problems facing the application to the hippocampus of a rec storage by an associative memory, achieved by insertion of a hidden layer. W biological constraints like the limited overall connectivity and the distrib of maps. We will show that the proposed multiple layer mechanism employing k-winner-take-all mechanism for the retrieval and completion of binary pat can be matched to the layers of the hippocampus, allowing some of its biologi in terms of the model. (C) 2002 Elsevier Science B.V. All rights reserved. C AMARI S, 1989, NEURAL NETWORKS, V2, P451 R AMIT DJ, 1989, MODELING BRAIN FUNCT ANDERSON JA, 1968, KYBERNETIK, V5, P113 ANDERSON JA, 1977, PSYCHOL REV, V84, P413 ARBIB MA, 1998, NEURAL ORG STRUCTURE BOSCH H, 1993, NEURAL NETWORKS, V11, P869 BROWN TH, 1979, SYNAPTIC ORG BRAIN, P346 BUSZAKI G, 1992, SCIENCE, V256, P1025 EICHENBAUM H, 1996, P NATL ACAD SCI USA, V93, P13500 FELLENZ WA, 1999, INT JOINT C NEUR NET, V2, P775 FOSTER J, 1997, COGNITIVE MODELS MEM, P275 FREUND TF, 1996, HIPPOCAMPUS, V6, P347 FUSTER JM, 1995, MEMORY CEREBRAL CORT GLUCK MA, 1997, NEUROBIOLOGY LEARNIN, P417 GRAHAM B, 1995, BIOL CYBERN, V72, P337 GRANGER R, 1996, HIPPOCAMPUS, V6, P567 HASSELMO ME, 1994, J NEUROSCI, V14, P3898 HASSOUN MH, 1995, ASS NEURAL MEMORIES HEIT G, 1988, NATURE, V333, P773 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 KANERVA P, 1988, SPARSE DISTRIBUTED M KOHONEN T, 1972, IEEE T COMPUT, V21, P353 KOSKO B, 1988, IEEE T SYST MAN CYB, V18, P49 LEVY WB, 1989, PSYCHOL LEARN MOTIV, V23, P243 LORINCZ A, 1998, BIOL CYBERN, V79, P263 MARR D, 1971, PHILOS T ROY SOC B, V262, P23 MCCLELLAND JL, 1995, PSYCHOL REV, V102, P419 MCNAUGHTON BL, 1987, TRENDS NEUROSCI, V10, P408 MOLL M, 1997, NEURAL NETWORKS, V10, P1017 MURRE JMJ, 1996, HIPPOCAMPUS, V6, P675 OKADA M, 1996, NEURAL NETWORKS, V9, P1429 PALM G, 1980, BIOL CYBERN, V36, P19 PATTON PE, 1995, HIPPOCAMPUS, V5, P245 RAWLINS JNP, 1985, BEHAV BRAIN SCI, V8, P479 ROLLS ET, 1989, NEURAL MODELS PLASTI, P240 ROLLS ET, 1998, NEURAL NETWORKS BRAI SCHWENKER F, 1996, NEURAL NETWORKS, V9, P445 SQUIRE LR, 1988, TRENDS NEUROSCI, V11, P170 STEINBUCH K, 1961, KYBERNETIK, V1, P36 TEYLER TJ, 1986, BEHAV NEUROSCI, V100, P147 TRAUB RD, 1999, FAST OSCILLATIONS CO VOGEL DD, 1998, NEURAL NETWORKS, V11, P897 WILLSHAW DJ, 1969, NATURE, V222, P960 WILLSHAW DJ, 1990, PHILOS T ROY SOC B, V329, P205 WU K, 1998, HIPPOCAMPUS, V8, P217 ZOLAMORGAN SM, 1990, SCIENCE, V250, P288 N 46 R P JAN D P Y V L B P E P U T E R P T A U T I S O A B 2003 50 31 50 ISI:000180567700004 J Lenze, B On a perceptron-type learning rule for higher order Hopficld neural networ translation NEUROCOMPUTING In the following, we will introduce a new Perceptron-like learning rule to e of higher order Hopfield neural networks without significant increase of the approach will lead to a generalized Perceptron learning rule which generates networks with dilation and translation that perform perfectly on the traini fulfills the so-called conditionally strong F-separability condition. In th satisfies a kind of optimality criterion which means that it finds appropr finite number of learning cycles in case that a solution exists. (C) 2002 Els reserved. C ANDERSON JA, 1972, MATH BIOSCI, V14, P197 R BALDI P, 1987, PHYS REV LETT, V58, P913 CHAO DY, 1993, COMPUT J, V36, P554 CHEN HH, 1986, AIP C P, P86 COHEN MA, 1983, IEEE T SYST MAN CYB, V13, P815 FAHNER G, 1994, NEURAL NETWORKS, V7, P279 GILES CL, 1987, APPL OPTICS, V26, P4972 GILES CL, 1988, NEURAL INFORMATION P, P301 GROSSBERG S, 1968, P NATIONAL ACADEMY S, V59, P368 HASSOUN MH, 1989, NEURAL NETWORKS, V2, P275 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 HOPFIELD JJ, 1984, P NATL ACAD SCI USA, V81, P3088 KAMP Y, 1990, RECURSIVE NEURAL NET KOHONEN T, 1972, IEEE T COMPUT, V21, P353 KOHONEN T, 1984, SELF ORG ASS MEMORY KOSKO B, 1987, APPL OPTICS, V26, P4947 KOSKO B, 1988, IEEE T SYST MAN CYB, V18, P49 LENZE B, 1996, ADV TOPICS MULTIVARI, P197 LENZE B, 1998, DISCRETE APPL MATH, V89, P169 LEUNG CS, 1993, IEEE T NEURAL NETWOR, V4, P879 LEUNG CS, 1994, IEEE T SYST MAN CYB, V24, P791 PSALTIS D, 1988, NEURAL NETWORKS, V1, P149 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386 ROSENBLATT F, 1962, PRINCIPLES NEURODYNA SHANMUKH K, 1993, P INT JOINT C NEUR N, V3, P2670 SOMMER FT, 1999, NEURAL NETWORKS, V12, P281 SRINIVASAN V, 1991, P INT JOINT C NEUR N, P2472 WANG T, 1994, IEEE T SYST MAN CYB, V24, P778 WANG YF, 1990, IEEE T NEURAL NETWOR, V1, P81 WANG YF, 1991, IEEE T NEURAL NETWOR, V2, P559 ZHANG BL, 1993, IEEE T NEURAL NETWOR, V4, P864 ZHUANG X, 1992, IEEE T NEURAL NETWOR, V2, P1010 N R P D P Y V L B P E P U T E R P T A U T I S O A B 32 OCT 2002 48 391 401 ISI:000178464600025 J Knoblauch, A Palm, G Scene segmentation by spike synchronization in reciprocally connected visu cortical feedback BIOLOGICAL CYBERNETICS To investigate scene segmentation in the visual system we present a model visual areas using spiking neurons. Area P corresponds to the orientation-sele visual cortex, while the central visual area C is modeled as associative memory according to Hebbian learning. Without feedback from area C, a single stimul and irregular activity, synchronized only for neighboring patches (slow state activity is faster with an enlarged synchronization range (fast state). Wh of several stimulus objects, scene segmentation happens on a time scale of alternating epochs of the slow and last states, where neurons representing the in the fast state. Correlation analysis reveals synchronization on differe experiments (designated as tower, castle, and hill peaks). On the fast tim frequency range), recordings from two sites coding either different or the sa that are either flat or exhibit oscillatory modulations with a central pea experimental findings, whereas standard phase-coding models would predict different objects. C ABELES M, 1982, LOCAL CORTICAL CIRCU R AERTSEN A, 1994, P S OSC EV REL BRAIN AERTSEN A, 1994, PHYSICA D, V75, P103 ANDERSON J, 2000, NAT NEUROSCI, V3, P617 ARIELI A, 1996, SCIENCE, V273, P1868 BARTSCH AP, 2001, BIOL CYBERN, V84, P41 BIBBIG A, 2001, J NEUROSCI, V21, P9053 BONHOEFFER T, 1991, NATURE, V353, P429 BONHOEFFER T, 1993, J NEUROSCI, V13, P4157 BRAITENBERG V, 1978, LECTURE NOTES BIOMAT, V21, P171 BRAITENBERG V, 1991, ANATOMY CORTEX STAT ECKHORN R, 1988, BIOL CYBERN, V60, P121 ECKHORN R, 1990, NEURAL COMPUT, V2, P293 ENGEL AK, 1991, P NATL ACAD SCI USA, V88, P6048 ENGEL AK, 1991, P NATL ACAD SCI USA, V88, P9136 ENGEL AK, 1991, SCIENCE, V252, P1177 FAHLE M, 1991, BIOL CYBERN, V66, P1 FRIEN A, 1994, NEUROREPORT, V5, P2273 GERSTNER W, 1993, BIOL CYBERN, V68, P363 GRAY CM, 1989, NATURE, V338, P334 HEBB D, 1949, ORG BEHAV NEUROPSYCH HOFFMANN K, 1996, NEUROWISSENSCHAFT, P405 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 HORN D, 1991, NEURAL COMPUT, V3, P510 KNOBLAUCH A, 1999, THESIS U ULM ULM KNOBLAUCH A, 2001, EMERGING NEURAL COMP, P407 KNOBLAUCH A, 2001, NEURAL NETWORKS, V14, P763 KNOBLAUCH A, 2002, BIOL CYBERN, V87, P168 KONIG P, 1991, NEURAL COMPUT, V3, P155 KONIG P, 1995, NEURAL COMPUT, V7, P469 KREITER AK, 1996, J NEUROSCI, V16, P2381 MACGREGOR R, 1987, NEURAL BRAIN MODELIN MALSBURG C, 1986, BIOL CYBERN, V54, P29 MORAN J, 1985, SCIENCE, V229, P782 NELSON JI, 1992, VISUAL NEUROSCI, V9, P21 NOWAK LG, 2000, TIME BRAIN, P53 PALM G, 1980, BIOL CYBERN, V36, P19 PALM G, 1982, NEURAL ASSEMBLIES AL PALM G, 1985, COGN SYST, V1, P107 PALM G, 1986, BRAIN THEORY, P211 PALM G, 1987, REAL BRAINS ARTIFICI PALM G, 1990, CONCEPTS NEUROSCI, V1, P133 PALM G, 1993, BRAIN THEORY REYNOLDS JH, 1999, NEURON, V24, P19 RITZ R, 1994, BIOL CYBERN, V71, P349 SCHILLEN TB, 1991, NEURAL COMPUT, V3, P167 SINGER W, 1995, ANNU REV NEUROSCI, V18, P555 SOMMER FT, 1999, NEURAL NETWORKS, V12, P281 TSODYKS M, 1999, SCIENCE, V286, P1943 VONDERMALSBURG C, 1986, BRAIN THEORY, P161 VONDERMALSBURG C, 1992, BIOL CYBERN, V67, P233 WENNEKERS T, 1995, SUPERCOMPUTING BRAIN, P301 WENNEKERS T, 1996, P INT C ART NEUR NET, P451 WENNEKERS T, 1997, THEOR BIOSCI, V116, P273 WENNEKERS T, 1999, NEUROCOMPUTING, V26, P579 WENNEKERS T, 2000, TIME BRAIN CONCEPTUA, P251 WILLSHAW DJ, 1969, NATURE, V222, P960 N R P D P Y V L I S B P E P U T E R P T A U T I S O 57 SEP 2002 87 3 151 167 ISI:000178406500001 J Knoblauch, A Palm, G Scene segmentation by spike synchronization in reciprocally connected visual and synchronization on larger space and time scales BIOLOGICAL CYBERNETICS A We present further simulation results of the model of two reciprocally con B in the first paper [Knoblauch and Palm (2002) Biol Cybern 87:151-167]. One orientation-selective subsystem of the primary visual cortex, the other is mo representing stimulus objects according to Hebbian learning. We examine the of our model on larger time and space scales. and relate it to experimental is achieved by attention switching on a time-scale longer than the gamma rang can vary depending on habituation parameters in the range of tens to hundreds process can be related to findings concerning attention and biased competi experimental poststimulus time histograms (PSTHs) of single neurons under attentional conditions. In a larger variant the model exhibits traveling w and fast time-scales, with properties similar to those found in experiments standard model is the tendency to produce anti-phase correlations for fast Increasing the inter-areal delays in our model produces alternations of in oscillations. The experimentally observed in-phase correlations can most n involvement of both fast and slow inter-areal connections; e.g., by two ax to fast-conducting myelinated and slow-conducting unmyelinated axons. 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A methodological investigation o I S NEUROIMAGE O A This paper presents an evaluation of a common approach that has been conside B exploratory fMRI data analyses. The approach includes two stages: creating partitions with increasing number of subsets (clustering) and selecting the that exhibits the clearest indications of an existing structure (cluster val that the selected partition is actually the best characterization of the dat were directed to find the most appropriate validity function(s). In our analys the sequence of partitions according to the given objective function. Our stu optimization of the partition, for one or more numbers of clusters, can easil result which, in turn, may lead the analyst to a misleading interpretation of a sufficient optimization, for each included number of clusters, provided the characterization of the data. Furthermore, it enabled an adequate evaluati These findings were obtained independently for three clustering algorithms (r clustering variant) and three up-to-date cluster validity functions. The findi of Gaussian clusters, simulated data sets that mimic typical fMRI response Based on our results we propose a number of options of how to configure im 2002 Elsevier Science (USA). C *MATHW INC, 1999, MATHL MATH SOFTW PAC R ALSULTAN KS, 1997, PATTERN RECOGN, V30, P2023 ANDERSEN AH, 1999, MAGN RESON IMAGING, V17, P795 ARFANAKIS K, 2000, MAGN RESON IMAGING, V18, P921 BAUMGARTNER R, 2000, MAGN RESON IMAGING, V18, P89 BAUNE A, 1999, NEUROIMAGE, V9, P477 BEZDEK JC, 1981, PATTERN RECOGNITION BEZDEK JC, 1992, FUZZY MODELS PATTERN BEZDEK JC, 1995, NEURAL NETWORKS, V8, P729 BILBRO GL, 1994, IEEE T SYST MAN CYB, V24, P684 BOOKHEIMER SY, 1995, HUM BRAIN MAPP, V3, P93 BOUDRAA AO, 1999, ELECTRON LETT, V35, P1606 BRUNSWICK N, 1999, BRAIN 10, V122, P1901 CHUANG KH, 1999, IEEE T MED IMAGING, V18, P1117 DIEBOLD F, 2000, NEUROIMAGE, V11 DUDA RO, 1973, PATTERN RECOGNITION EQUITZ WH, 1989, IEEE T ACOUST SPEECH, V37, P1568 FADILI MJ, 2001, MED IMAGE ANAL, V5, P55 FILZMOSER P, 1999, MAGN RESON IMAGING, V17, P817 FISCHER H, 1999, MAGNET RESON MED, V41, P124 FRANTI P, 1998, PATTERN RECOGN, V31, P1139 FUKAYAMA Y, 1989, P 5 FUZZ SYST S, P247 GERSHO A, 1997, VECTOR QUANTIZATION GEVA AB, 2000, PATTERN RECOGN LETT, V21, P511 GOUTTE C, 1999, NEUROIMAGE, V9, P298 HAGOORT P, 1999, J COGNITIVE NEUROSCI, V11, P383 HWANG WJ, 1998, IEICE T INF SYST ED, V81, P616 JAIN AK, 1988, ALGORITHMS CLUSTERIN JARMASZ M, 1998, P ISMRM 6 ANN M SYDN, P2068 JARMASZ M, 2000, HUM BRAIN MAPP M 200 JARMASZ M, 2001, NEUROIMAGE S 2, V13, S163 JIANG T, 2000, P IEEE INT S BIOINF, P223 KIM DJ, 2001, IEICE T INF SYST ED, V84, P281 KIM SG, 1997, J NEUROSCI METH, V74, P229 KOTHARI R, 1999, PATTERN RECOGN LETT, V20, P405 KWEON SH, 1999, ELECTRON LETT, V34, P2176 LAI SH, 1999, MAGN RESON IMAGING, V17, P827 LEVIN DN, 2001, NEUROIMAGE, V13, P153 LINDE Y, 1980, IEEE T COMMUN, V28, P85 MOLLER U, 1998, IEEE T SIGNAL PROCES, V46, P2515 MOLLER U, 2001, NEUROIMAGE 1, V14, P206 NAKAMURA E, 1998, PATTERN RECOGN LETT, V19, P1265 NGAN SC, 1999, MAGNET RESON MED, V41, P939 ONG SH, 2000, PATTERN RECOGN LETT, V21, P365 PAL NR, 1995, IEEE T FUZZY SYST, V3, P370 PAULESU E, 1993, NATURE, V362, P342 PENA JM, 1999, PATTERN RECOGN LETT, V20, P1027 PIZZI NJ, 2001, ARTIF INTELL MED, V21, P263 PUZICHA J, 2000, PATTERN RECOGN, V33, P617 REZAEE MR, 1998, PATTERN RECOGN LETT, V19, P237 ROBERTS SJ, 2000, PATTERN RECOGN, V33, P833 SERGENT J, 1992, CEREB CORTEX, V2, P68 SOMORJAI R, 2001, ARTIFICIAL INTELL ME SVENSEN M, 2000, P 7 INT C NEUR INF P, P88 TECCHIOLLI GP, 1991, BIOL CYBERN, V65, P501 TSENG LY, 2000, PATTERN RECOGN, V33, P1251 WICHERT A, 2001, NEUROIMAGE S 2, V13, S282 WINDHAM MP, 1982, IEEE T PATTERN ANAL, V4, P357 WISMULLER A, 2001, INT J COMPUT VISION, V46, P103 XUANLI LX, 1991, IEEE T PATTERN ANAL, V13, P841 ZAHID N, 1999, PATTERN RECOGN LETT, V20, P123 ZAHID N, 1999, PATTERN RECOGN, V32, P1089 N R P D P Y V L I S B P E P U T E R 62 SEP 2002 17 1 431 446 ISI:000178102000033 P J T A Cordes, D U Haughton, V Carew, JD Arfanakis, K Maravilla, K T Hierarchical clustering to measure connectivity in fMRI resting-state data I S MAGNETIC RESONANCE IMAGING O A Low frequency oscillations, which-are temporally correlated in functionall B characterize the mammalian brain, even when no explicit cognitive-tasks ar connectivity MR imaging is used to map regions of the resting brain showing s fluctuations in cerebral blood flow and oxygenation. In this study, we use a h to detect similarities of low-frequency fluctuations. We describe one measu frequency range for classification of resting-state. fMRI data. Furthermor contribution of motion and hardware instabilities to resting-state correla reduce artifacts. For all cortical regions studied and clusters obtained, contamination of functional connectivity maps by the respiratory and cardiac patterns of functional connectivity can be obtained with hierarchical clus neuronal connections. The corresponding voxel time series do not show sign respiratory or cardiac frequency band. (C) 2002 Elsevier Science Inc. All C *MATHW INC, MATL STAT TOOLB R ARFANAKIS K, 2000, MAGN RESON IMAGING, V18, P921 BAUNE A, 1999, NEUROIMAGE, V9, P477 BISWAL B, 1995, MAGNET RESON MED, V34, P537 BRESSLER SL, 1997, 9701 ASOCS, P37 BRESSLER SL, 2001, TRENDS COGN SCI, V5, P26 CORDES D, 1999, P 7 ANN M ISMRM PHIL, P1706 CORDES D, 2000, AM J NEURORADIOL, V21, P1636 CORDES D, 2001, AM J NEURORADIOL, V22, P1326 COX RW, 1996, COMPUT BIOMED RES, V29, P162 COX RW, 1999, MAGNET RESON MED, V42, P1014 DREVETS WC, 1992, J NEUROSCI, V12, P3628 FILZMOSER P, 1999, MAGN RESON IMAGING, V17, P817 FRISTON KJ, 1993, J CEREBR BLOOD F MET, V13, P5 FRISTON KJ, 1995, CLIN NEUROSCI, V3, P89 GESCHWIND N, 1965, BRAIN, V88, P231 GILLER CA, 1999, J CEREBR BLOOD F MET, V19, P452 GOUTTE C, 1999, NEUROIMAGE, V9, P298 GOUTTE C, 2001, HUM BRAIN MAPP, V13, P165 HARTIGAN JA, 1975, CLUSTERING ALGORITHM HAUGHTON VM, 1998, OXYGEN TRANSPORT TIS, P583 HOFFMAN RE, 1993, SCHIZOPHRENIA BULL, V19, P119 HYDE JS, 1999, MED RADIOLOGY DIAGNO, P263 KIVINIEWI V, 2001, P ISMRM 9 ANN M GLAS, P1708 LOWE MJ, 1997, MAGNET RESON MED, V37, P723 LOWE MJ, 1998, NEUROIMAGE, V7, P119 LOWE MJ, 1999, P 7 ANN M INT SOC MA, P1711 MADSEN EL, 1991, MED PHYS, V18, P549 MALDJIAN JA, 2001, AM J NEURORADIOL, V22, P239 MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P160 OBRIG H, 2000, NEUROIMAGE, V12, P623 QUIGLEY M, 2001, AM J NEURORADIOL, V22, P294 RANDALL PL, 1983, MED HYPOTHESES, V10, P247 SMITH AM, 1999, NEUROIMAGE, V9, P526 STEIN T, 2000, AM J NEURORADIOL, V21, P1397 WEINBERGER DR, 1992, AM J PSYCHIAT, V149, P890 XIONG JH, 1999, HUM BRAIN MAPP, V8, P151 N R P D P Y V L I S B P E P U T E R 37 MAY 2002 20 4 305 317 ISI:000177489400001 P J T A Dodel, S U Herrmann, JM Geisel, T T Functional connectivity by cross-correlation clustering I S NEUROCOMPUTING O A In addition to information on localization of brain functions, data from f B cues about the functional connectivity among modular units, We propose a d clustering algorithm based on temporal cross-correlations and elements of functionally connected regions. The cluster concept can be changed in a co functional connectivity structure in detail. The algorithm is applied to da to successfully determine clusters related to the stimulus. Furthermore, t include the analysis of temporal relations between different brain regions. Science B.V. C ARFANAKIS K, 2000, MAGN RESON IMAGING, V18, P921 R BAUMGARTNER R, 1998, MAGN RESON IMAGING, V16, P115 BAUNE A, 1999, NEUROIMAGE, V9, P477 N 3 R P JUN D P Y V L B P E P U T E R P T A U T I S O A B 2002 44 1065 1070 ISI:000176839200154 J Booth, V Bose, A Burst synchrony patterns in hippocampal pyramidal cell model networks NETWORK-COMPUTATION IN NEURAL SYSTEMS Types of, mechanisms for and stability of synchrony are discussed in the c pyramidal cell and interneuron model networks. We show how the strength an excitatory synaptic inputs work together to produce either perfectly synchr oscillations, across different burst or spiking modes of firing. The analysi tend to desynchronize cells, and how common, slowly decaying inhibition can We also introduce the concept of 'equivalent networks' in which networks wit synaptic connections display identical firing patterns. 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This can be achieved by allowing new inf in a so-called palimpsest memory. This paper describes an incremental learni confidence propagation neural network that has palimpsest properties when em network. The network does not suffer from catastrophic forgetting, has a capa time constant and exhibits faster convergence for newer patterns. 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When there are several parti in a visual scene one has to have an internal knowledge about the object t associative memories. We have studied the bidirectional dynamical interaction of two areas, wher to match area V1 in greater detail and the higher area uses Hebbian learning for a number of geometric shapes. Both areas are modelled with simple spiking of "binding" by spike-synchronisation and of the effects of Hebbian learning i (including the long-range cortico-cortical projections) are studied. Presenting a superposition of three stimulus objects corresponding to learned two states of activity: (i) relatively slow and unordered activity, synchroni and (ii) faster oscillations, synchronized over larger regions. The neuron gr tended to be simultaneously in either the slow or the fast state. At each part was found to be in the fast state. Activation of the three assemblies swit milliseconds. 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When humans pr categories, sets of cortical areas become active differentially. The meani aspects of its reference, may be crucial for determining which set of cort process! ng. Word-related neuron webs with specific cortical distributions category-specific differences in brain activity. Neuroscientific principles topographies. 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The effects of contrast-to-noise r hemodynamic response within a brain region, time lags of the responses among simulated. activation locations on the ICA were investigated in the simulat from the auditory sentence-monitoring experiments in each subject using IC in bilateral auditory and language cortices, as well as in superior sensorimo previous PET studies. The associated time courses in the activated brain regi of the sentence presentation, as evidenced by the recorded button-press resp component ordering that may rank highly the components of primary interest developed. The simulation results characterized the performance of ICA und provide useful information for experimental design and data interpretation C AGUIRRE GK, 1998, NEUROIMAGE, V8, P360 R AMARI S, 1996, ADV NEUR IN, V8, P757 BANDETTINI PA, 1992, MAGNET RESON MED, V25, P390 BANDETTINI PA, 1993, MAGNET RESON MED, V30, P161 BANDETTINI PA, 2000, FUNCTIONAL MRI, P205 BAUMGARTNER R, 1998, MAGN RESON IMAGING, V16, P115 BAUMGARTNER R, 2000, MAGN RESON IMAGING, V18, P89 BAUNE A, 1999, NEUROIMAGE, V9, P477 BELL AJ, 1995, NEURAL COMPUT, V7, P1129 BISWAL BB, 1999, J COMPUT ASSIST TOMO, V23, P265 BUCKNER RL, 1998, HUM BRAIN MAPP, V6, P373 COHEN J, 1988, STAT POWER ANAL BEHA COHEN MS, 1997, NEUROIMAGE, V6, P93 COMON P, 1994, SIGNAL PROCESS, V36, P11 CORDES D, 1999, P 7 ANN M ISMRM PHIL, P1706 FILZMOSER P, 1999, MAGN RESON IMAGING, V17, P817 FISCHER H, 1999, MAGNET RESON MED, V41, P124 FRISTON K, 1999, HUM BRAIN MAPP, V8, P92 FRISTON KJ, 1993, J CEREBR BLOOD F MET, V13, P5 FRISTON KJ, 1994, HUMAN BRAIN MAPPING, V1, P153 FRISTON KJ, 1995, HUMAN BRAIN MAPPING, V2, P189 FRISTON KJ, 1995, NEUROIMAGE, V2, P45 GOLAY X, 1998, MAGNET RESON MED, V40, P249 GOUTTE C, 1999, NEUROIMAGE, V9, P298 HU XP, 1995, MAGNET RESON MED, V34, P201 HYVARINEN A, 2000, NEURAL NETWORKS, V13, P411 KWONG KK, 1992, P NATL ACAD SCI USA, V89, P5675 LABAR KS, 1998, NEURON, V20, P937 LAI SH, 1999, MAGN RESON IMAGING, V17, P827 LE TH, 1995, P SMR, P820 LEE TW, 1999, NEURAL COMPUT, V11, P409 LIU TT, 2000, P 8 ANN M ISMRM DENV, P847 MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P160 MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P368 MCKEOWN MJ, 1998, P NATL ACAD SCI USA, V95, P803 NGAN SC, 1999, MAGNET RESON MED, V41, P939 OGAWA S, 1990, P NATL ACAD SCI USA, V87, P9868 OGAWA S, 1992, P NATL ACAD SCI USA, V89, P5951 OJEMANN JG, 1997, NEUROIMAGE, V6, P156 SILBERSWEIG DA, 1993, J CEREBR BLOOD F MET, V13, P617 SILBERSWEIG DA, 1994, J CEREBR BLOOD F MET, V14, P771 WORSLEY KJ, 1995, NEUROIMAGE, V2, P173 XIONG JH, 1995, HUM BRAIN MAPP, V3, P287 YANG YH, 2000, MAGNET RESON MED, V43, P185 N R P D P Y V L I S B P E P U T E R 44 DEC 2001 14 6 1432 1443 ISI:000172524500020 P S T A Payrits, S U Szatmary, Z Zalanyi, L Erdi, P T Use of parallel computers in neurocomputing I S RECENT ADVANCES IN PARALLEL VIRTUAL MACHINE AND MESSAGE PASSING INTERFACE, O S LECTURE NOTES IN COMPUTER SCIENCE E A Large-scale simulation of brain activity is based on a general theory withi B field theory. The theory and algorithm developed is now implemented to a c computational capacity the simulation of normal and pathological cortical possible. C ARBIB M, 1997, NEURAL ORG STRUCTURE R BARNA G, 1998, BIOL CYBERN, V79, P308 KOTTER R, 2000, PHILOS T ROY SOC B, V355, P127 SZATMARY Z, 2000, THESIS TU BUDAPEST VENTRIGLIA F, 1974, B MATH BIOL, V36, P534 N 5 R P 2000 Y V 1908 L B 313 P E 321 P U ISI:000171904500043 T E R P J T A Kotter, R U Stephan, KE Palomero-Gallagher, N Geyer, S Schleicher, A Zilles, K T Multimodal characterisation of cortical areas by multivariate analyses of rec I data S ANATOMY AND EMBRYOLOGY O A Cortical areas are regarded as fundamental structural and functional units wit B networks of the brain. Their properties have been described extensively by chemoarchitectonics, cortical and extracortical connectivity patterns, recep properties, lesion effects, and other structural and functional characteri approaches aiming at multimodal characterisations of cortical areas or at the of the cortical network, however, are still scarce and usually limited to as cytoarchitectonical or tract tracing data. Here we describe a methodologica evaluation, comparison and integration of different data modalities from t practical application and significance in the analysis of receptor binding the motor and visual cortices of macaque monkeys. The framework builds on a data between different cortical parcellation schemes, as well as on statis exploration of multivariate data sets comprising data of different types and a relationship between intrinsic area properties as expressed by quantitat extrinsic inter-area communication, which relies on anatomical connectivit preliminary evidence for a good correspondence of these two data types in the m discrepancy in the visual cortex, raising hypotheses about the different organ by receptors and connectivity. The methodological framework presented here is a wide range of further data modalities, and is specific enough to permit n concerning brain organisation. Thus, this approach promises to be very use characterise multimodal structure - function relationships in the brain. 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Location of areas 44 (in rod) and 45 (in yellow) in two exemplary cor brain and details of the lateral views of the hemispheres after three-dimensio rendering. Left in the brain is left in the image. artf = ascending branch = horizontal branch of the lateral fissure; ifs = inferior frontal sulcus; p also Fig. 6 in article by Amunts and Zilles.) Figure 2. Population maps of two different cortical areas superimposed on Brodmann's area 17 (visual cortex) and 45 (part of Broca's region). The ov voxel of the standard brain. (e.g., yellow = overlap of seven (out of 10) 10 brains. Orientation of the brain according to the AC-PC line.(126) Nonl the overlap of the individual cortical areas in the reference space when c of only linear tools (compare left with middle section at z = -5). Both reg set of brains, however, intersubject variability is larger in area 45 than in article by Amunts and Zilles.) 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As a possible solution, the temporal correlation and implemented in phase-coding models. We propose an alternative model th findings of synchronized and desynchronized fast oscillations more closely. T considerations concerning improved pattern separation in associative memorie properties of the visual cortex on the other. It consists of two reciproca corresponding to a peripheral visual area (P), the other a central associati orientation-selective subsystem of the primary visual cortex, while C was mo with connections formed by Hebbian learning of all assemblies corresponding neurons including habituation and correlated noise were incorporated as well Three learned stimuli were presented simultaneously and correlation analys recordings. Generally, we found two states of activity: (i) relatively slo at about 20-25 Hz, synchronized only within small regions; and (ii) faster around 50-60 Hz, synchronized over the whole simulated area. The neuron gro tended to be simultaneously in either the slow or the fast state. At each part was found to be in the fast state. Activation of the three assemblies switc This can be interpreted as self-generated attention switching. On the time oscillations, cross correlations between local neuron groups were either m correlograms resulted if the groups coded features corresponding to a comm correlograms remained flat. This behavior is in agreement with experimental re would generally predict modulated correlations also in the case of different o a technical version from our biological associative memory model that accompl parallel in O(log(2) n) steps for n neurons and sparse coding. (C) 2001 Els reserved. 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Iss. SI 763 780 ISI:000171417300016 J Lenze, B Improving Leung's bidirectional learning rule for associative memories IEEE TRANSACTIONS ON NEURAL NETWORKS In 1994, Leung introduced a Perceptron-like learning rule to enhance the recal associative memories (BAMs). He proved that his so-called bidirectional le solution within a finite number of learning iterations in case that a solut the setting of Leung a solution only exists in case that the training set by hyperplanes through the origin (both with respect to input and output pat the field. We will extend Leung's approach by considering conditionally st allowing separating hyperplanes not containing the origin. Moreover, we wi generalized by defining so-called dilation and translation parameters enla leaving their complexity almost unaffected. The whole approach will lead t learning rule which generates BAMs with dilation and translation that perf set in case that the latter satisfies the conditionally strong linear separa in the sense of Leung, we will end up with an optimal learning strategy wh idea as a special case. 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According to current theories, schizophrenia results from alte B circuits for fundamental cognitive operations. Consequently, the poorly un neuroleptic treatment may be explainable by altered functional interaction 'cognitive dysmetria' model hypothesizes that one key structure in these c investigate the effects of olanzapine on cerebellar functional connectivity ( analysis (SVCA) was used in a functional magnetic resonance imaging (fMRI) st task, Methods. fMRI scans were obtained from six schizophrenic patients under bo olanzapine-treated conditions and from a matched control group of six heal time points. SVCAs were performed for anatomically and functionally standardi cerebellum. SVCA results were then processed by three different randomizat Results. The analyses revealed that olanzapine caused widespread changes of CF in prefrontal cortex and mediodorsal thalamus. Significant changes in moto subtractions within both groups and may thus indicate repetition effects r Olanzapine 'normalized' the patients' CFC patterns for the right, but not Conclusion. Even for a simple motor task, olanzapine affects functional inter and many non-motor brain regions, including elements of the 'cognitive dys our findings suggest that olanzapine has a stronger differential effect on cortex and thalamus than in motor structures. 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S S61 S61 ISI:000169106300062 P J T A Moller, U U Ligges, M Grunling, C Georgiewa, P Kaiser, WA Witte, H Blanz, B T Pitfalls in the clustering of neuroimage data and improvements by global o I S NEUROIMAGE O A In this paper, we examined three vector quantization (VQ) methods used for th B (clustering) of functional magnetic resonance imaging (fMRI) data. Classif volume element (voxel), according to a given scanning raster, was assigned on similarity of the fMRI signal patterns. It was investigated how the VQ that describes the region involved in a particular brain function. As an e stimulated by a word comparison task. VQ analysis methodology was verified patterns. It was demonstrated in detail that VQ based on global rather tha objective function yielded a higher performance. Performance was measured in of VQ attempts using several indices for goodness, reliability and efficiency it was shown that a poor local optimization caused either an underestimatio stimulus-induced brain activation. However, this was not observed if the cl a global optimization strategy. (C) 2001 Academic Press. C *SPM, STAT PAR MAPP R ANDERBERG MR, 1982, CLUSTER ANAL APPL BAUMGARTNER R, 2000, MAGN RESON IMAGING, V18, P89 BAUNE A, 1999, NEUROIMAGE, V9, P477 BRUNSWICK N, 1999, BRAIN 10, V122, P1901 ERBERICH SG, 1999, NEUROIMAGE, V9, S20 EVERITT BS, 1993, CLUSTER ANAL FILZMOSER P, 1999, MAGN RESON IMAGING, V17, P817 FISCHER H, 1999, MAGNET RESON MED, V41, P124 FRISTON KJ, 1995, HUMAN BRAIN MAPPING, V2, P165 GERSHO A, 1997, VECTOR QUANTIZATION GOUTTE C, 1999, NEUROIMAGE, V9, P298 HAIG AR, 1995, ELECTROEN CLIN NEURO, V94, P288 HARTIGAN JA, 1973, CLUSTERING ALGORITHM JAIN A, 1988, ALGORITHMS CLUSTERIN KAUFMAN L, 1990, FINDING GROUPS DATA KIRKPATRICK S, 1983, SCIENCE, V220, P671 MARCHINI JL, 2000, NEUROIMAGE, V12, P366 MOLLER U, 1996, EEG-EMG-Z ELEK ELEKT, V27, P105 MOLLER U, 1998, IEEE T SIGNAL PROCES, V46, P2515 MUZIK O, 2000, NEUROIMAGE, V12, P538 NGAN SC, 1999, MAGNET RESON MED, V41, P939 ORRISON WW, 1995, FUNCTIONAL BRAIN IMA PHILLIPS WE, 1995, MAGN RESON IMAGING, V13, P277 PRICE CJ, 1997, NEUROIMAGE 1, V5, P261 SIMMONS A, 1996, MAGN RESON IMAGING, V14, P73 SOLIS FJ, 1981, MATH OPNS RES, V6, P19 SPAETH H, 1980, CLUSTER ANAL ALGORIT SUCKLING J, 1999, MAGN RESON IMAGING, V17, P1065 TANG KS, 1998, IEEE SIGNAL PROCESS, P22 UUTELA K, 1998, IEEE T BIO-MED ENG, V45, P716 WISMULLER A, 1998, BILDVERARBEITUNG MED, P402 ZEGER K, 1992, IEEE T SIGNAL PROCES, V40, P310 N R P D P Y V L I S P N B P E P U T E R 33 JUL 2001 14 1 Part 1 206 218 ISI:000169498000021 P J T A Sagi, B U Nemat-Nasser, SC Kerr, R Hayek, R Downing, C Hecht-Nielsen, R T A biologically motivated solution to the cocktail party problem I S NEURAL COMPUTATION O A We present a new approach to the cocktail party problem that uses a cortro B architecture (Hecht-Nielsen, 1998) as the front end of a speech processing in three important respects. First, our method assumes and exploits detaile wish to attend to in the cocktail party environment. Second, our goal is to pr of a pattern recognition system rather than to separate one or more of the mix the neural network model we employ is more biologically feasible than are cocktail party problem. Although the focus here is on the cocktail party p in this study can be applied to other areas of information processing. 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We show how visualization of fMRI time-courses may be structure. We consider fMRI time-courses (TCs) as points in multidimension vivo data, we show that minimum spanning tree (MST)-based sequencing of mu combination with a homogeneity map visualization, allows for effective and the groups of coactivated time-courses obtained by temporal clustering. Thi for investigation of brain connectivity. We also suggest a simple overall di set. (C) 2001 Elsevier Science Inc. All rights reserved. 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integration of the activity of specialized neur interactions. These interactions produce patterns of temporal correlations involving distributed neuronal populations, both within and across cortica computational studies suggest that changes in functional connectivity may and cognitive states and involve the integration of information across spe The interplay between functional segregation and integration can be quantitat from statistical information theory, in particular by defining a measure of measures the extent to which a pattern of functional connectivity produced by system combines the dual requirements of functional segregation and integr neuroanatomical motifs are uniquely associated with high levels of complex embedded in the pattern of long-range cortico-cortical pathways linking segr cerebral cortex. Our theoretical findings offer new insight into the intri connectivity and complexity in the nervous system. 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We that aid the discovery of interesting associations and patterns between br data. We consider several applications of these methods, such as the analy lesion-deficit, and structure morphological variability; the development o tumour analysis. We include examples of applications to real brain data. Sev as that of method validation or verification, are also discussed. 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NIs may he employed as preproc the interesting TCs prior to any further exploratory or confirmatory approa Inc. All rights reserved. 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They may be considered as hypothesis generating methods. The repr produce may be viewed as alternative hypotheses to the null hypothesis, ie here a resampling technique to validate the results of exploratory fuzzy clu an alternative hypothesis is represented by a cluster centroid. For both sim magnetic resonance imaging data, we show that by permutation-based resampl may be computed for each voxel belonging to a cluster of interest without assumptions. (C) 2000 Wiley-Liss, Inc. C BAUMGARTNER R, 1998, MAGN RESON IMAGING, V16, P115 R BAUMGARTNER R, 1999, IN PRESS MAGN RESON BAUMGARTNER R, 1999, NEUROIMAGE S, V9, P49 BAUNE A, 1999, NEUROIMAGE, V9, P477 BULLMORE E, 1996, MAGNET RESON MED, V35, P261 CARPENTER PA, 1999, TOP MAGN RESON IMAG, V10, P16 DEZDEK J, 1981, PATTERN RECOGNITION FISCHER H, 1999, MAGNET RESON MED, V41, P124 FORD I, 1995, J CEREBR BLOOD F MET, V15, P371 FRISTON KJ, 1993, J CEREBR BLOOD F MET, V13, P5 GOLAY X, 1998, MAGNET RESON MED, V40, P249 GOOD P, 1993, SPRINGER SERIES STAT GORDON HL, 1992, PROTEINS, V14, P249 GOUTTE HC, 1999, NEUROIMAGE, V9, P298 GRISWOLD M, 1999, P ISMRM PHIL PA, P1707 HOLMES AP, 1996, J CEREBR BLOOD F MET, V16, P7 JARMASZ M, 1998, P ISMRM 6 ANN M SYDN, P2068 LANGE N, 1997, APPL STAT-J ROY ST C, V46, P1 LANGE N, 1999, NEUROIMAGE 1, V10, P282 MCKEOWN MJ, 1998, HUM BRAIN MAPP, V6, P160 NGAN SC, 1999, MAGNET RESON MED, V41, P939 SCARTH GB, 1995, P INT SOC MAGN RES M, P238 SOMORJAI R, 1999, NEUROIMAGE S, V9, P45 SOMORJAI R, 1999, NEUROIMAGE S, V9, P46 N 24 R P FEB D P 2000 Y V 11 L I 2 S B 228 P E 231 P U ISI:000086078100023 T E R P J T A Baumgartner, R U Ryner, L Richter, W Summers, R Jarmasz, M Somorjai, R T Comparison of two exploratory data analysis methods for fMRI: fuzzy cluste I analysis S MAGNETIC RESONANCE IMAGING O A Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and B (PCA) may be considered as hypothesis-generating procedures that are comple statistical inferential methods in functional magnetic resonance imaging (fMR FCA and PCA is presented in a systematic fMRI study, with MR data acquired un no activation, with different noise contributions and simulated, varying " contrast-to-noise (CNR) ratio ranged between 1-10. We found that if fMRI d noise only, FCA and PCA show comparable performance. In the presence of othe (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of i for low CNR values. The comparison method that we introduced may be used t approaches such as independent component analysis or neural network-based te 2000. Published by Elsevier Science Inc. C BASILEVSKI A, 1994, STAT FACTOR ANAL REL R BAUMGARTNER R, 1998, MAGN RESON IMAGING, V16, P115 BAUNE A, 1999, NEUROIMAGE, V9, P477 BEZDEK J, 1981, PATTERN RECOGNITION CONSTABLE RT, 1995, MAGNET RESON MED, V34, P57 FILZMOSER P, 1999, MAGN RESON IMAGING, V17, P817 FISCHER H, 1999, MAGNET RESON MED, V41, P124 FORD I, 1995, J CEREBR BLOOD F MET, V15, P371 FRISTON KJ, 1993, J CEREBR BLOOD F MET, V13, P5 FRISTON KJ, 1995, NEUROIMAGE, V2, P45 GOLAY X, 1998, MAGNET RESON MED, V40, P249 GORDON HL, 1992, PROTEINS, V14, P249 GOUTTE C, 1999, NEUROIMAGE, V9, P298 JARMASZ M, 1998, 6 SCI M SOC MAGN RES, V3, P2068 KOHONEN T, 1995, SELF ORG MAPS LANGE N, 1996, STAT MED, V15, P389 LANGE N, 1997, APPL STAT-J ROY ST C, V46, P1 MCKEOWN M, 1998, HUM BRAIN MAPP, V3, P160 SAMAL M, 1998, NUCL MED COMMUN, V19, P161 SCARTH G, 1995, 12 ANN M SOC MAGN RE, V1, P238 SCARTH G, 1996, 4 SCI M SOC MAGN RES, V3, P1782 SIJBERS A, 1998, THESIS U ANTWERP SOMORJAI R, 1999, 7 SCI M SOC MAGN RES, P1718 SYCHRA JJ, 1994, MED PHYS, V21, P193 ZARAHN E, 1997, NEUROIMAGE, V5, P179 N R P D P Y V L I S B P E P U T E R P T A U T I S O A B 25 JAN 2000 18 1 89 94 ISI:000084587200010 J Liou, CY Yuan, SK Error tolerant associative memory BIOLOGICAL CYBERNETICS We present a new approach to enlarging the basin of attraction of associat auto-associative memory and temporal associative memory. The memory traine tolerate and recover from seriously noisy patterns. Simulations show that thi the number of limit cycles. C BRUCK J, 1990, P IEEE, V78, P1579 R COVER TM, 1965, IEEE T ELECTRON COMP, V14, P326 KOSKO B, 1987, APPL OPTICS, V26, P4947 KOSKO B, 1988, IEEE T SYST MAN CYB, V18, P49 LITTLE WA, 1974, MATH BIOSCI, V19, P101 LITTLE WA, 1975, BEHAVIORAL BIOL, V14, P115 MAZZA C, 1997, NEURAL NETWORKS, V10, P593 PORAT S, 1989, BIOL CYBERN, V60, P335 SCHWENKER F, 1996, NEURAL NETWORKS, V9, P445 TANK DW, 1987, SCI AM, V257, P104 XU ZB, 1996, NEURAL NETWORKS, V9, P483 N R P D P Y V L I S B P E P U T E R P T A U T I S O A B 11 OCT 1999 81 4 331 342 ISI:000083208800005 J Slorkey, AJ Valabregue, R The basins of attraction of a new Hopfield learning rule NEURAL NETWORKS The nature of the basins of attraction of a Hopfield network is as importan learning rule is re-introduced. This learning rule has a higher capacity th still keeps important functionality, such as incrementality and locality, wh However the basins of attraction of the fixed points of this learning rule ha important characteristics of basins of attraction are considered: indirect an distribution of sizes of basins of attraction and the shape of the basins o the new learning rule are compared with those of the Hebb rule. The size o of attractions are generally larger for the new rule than for the Hebb rul is more even, and the shape of the basins more round. (C) 1999 Elsevier Scie C AMARI S, 1988, NEURAL NETWORKS, V1, P63 R CHANG JY, 1993, ELECTRON LETT, V29, P2128 FORREST BM, 1988, J PHYS A, V21, P245 GRIMMETT GR, 1982, PROBABILITY RANDOM P HO AMCL, 1995, LECT NOTES COMPUT SC, V930, P202 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 HORNER H, 1989, Z PHYS B CON MAT, V76, P381 KANTER I, 1987, PHYS REV A, V35, P380 KRAUTH W, 1987, J PHYS A, V20, L745 MCELIECE RJ, 1987, IEEE T INFORM THEORY, V33, P461 SCHWENKER F, 1996, NEURAL NETWORKS, V9, P445 STORKEY A, 1997, ELECTRON LETT, V33, P1803 STORKEY AJ, 1997, LECT NOTES COMPUTER, V1327, P451 STORM G, 1998, CLIN CANCER RES, V4, P111 VIANA L, 1993, J PHYS I, V3, P777 WANG T, 1994, NEURAL NETWORKS, V7, P1379 YAU HW, 1991, J PHYS A-MATH GEN, V24, P5639 YAU HW, 1992, PHYSICA A, V185, P471 N R P D P Y V L I S B P E P U T E R P T A U T I S O A B 18 JUL 1999 12 6 869 876 ISI:000082104300007 J Billard, A Hayes, G DRAMA, a connectionist architecture for control and learning in autonomous ADAPTIVE BEHAVIOR Adaptation to their environment is a fundamental capability for living agents could also benefit. This work proposes a connectionist architecture, DRAMA, fo of autonomous robots. DRAMA stands for dynamical recurrent associative mem time-delay recurrent neural network, using Hebbian update rules. It allows regularities and time series in discrete sequences of inputs, in the face of The first part of this paper gives the mathematical description of the arc theoretically and through numerical simulations its performance. The secon on the implementation of DRAMA in simulated and physical robotic experimen the DRAMA architecture is computationally fast and inexpensive, which makes t for controlling 'computationally-challenged' robots. In the experiments, av with very limited computational capability and show that our robot can car and on-line learning of relatively complex cognitive tasks. In these exper wander randomly in a fixed environment, collecting information about its elem information of their sensors and actuators, they learn about physical regu experience of varying stimuli. The agents learn also from their mutual interact scenario, based on mutual following of the two agents, to enable transmiss robot to the other. C ASADA M, 1997, P 1 INT WORKSH COOP, P127 R BILLAD A, 1998, LEARNING ROBOTS MULT, P79 BILLARD A, ADAPTIVE BEHAV SPEC BILLARD A, 1996, 43 U ED BILLARD A, 1997, P 6 EUR WORKSH LEARN, P137 BILLARD A, 1998, CPM9838 CTR POL MOD BILLARD A, 1998, ROBOT AUTON SYST, V24, P71 BILLARD A, 1998, THESIS U EDINBURGH BUCKINGHAM J, 1992, NETWORK-COMP NEURAL, V3, P407 CHAUVIN Y, 1995, BACK PROPAGATION THE DAUTENHAHN K, 1995, ROBOT AUTON SYST, V16, P333 DAY SP, 1993, IEEE T NEURAL NETWOR, V4, P348 ELMAN JL, 1990, COGNITIVE SCI, V14, P179 FLOREANO D, 1996, IEEE T SYST MAN CY B, V26, P396 GAUSSIER P, 1998, APPL ARTIFICIAL INTE, V7 GILES CG, 1994, IEEE T NEURAL NETWOR, V52, P153 GRAHAM B, 1995, BIOL CYBERN, V72, P337 GRAHAM B, 1996, P INT C ART NEUR NET, P35 GROSSBERG S, 1992, COGNITIVE BRAIN RES, V1, P3 HATTORI M, 1996, NEUROCOMPUTING, V12, P1 HINTON GE, 1986, PARALLEL DISTRIBUTED, V1, P282 JORDAN MI, 1986, P 8 ANN C COGN SCI S, P531 KLINGSPOR V, 1997, APPL ARTIF INTELL, V11, P719 KOHONEN T, 1989, SELF ORG ASS MEMORY KOLEN JF, 1991, P 13 ANN C COGN SCI, P7 KUIPERS B, 1987, AAAI WORKSH SPAT REA, P774 KURZ A, 1996, IEEE T SYSTEMS, V26, P40 LIN DT, 1993, P WORLD C NEUR NETW, V1, P291 MATARIC MJ, 1997, AUTON ROBOT, V4, P73 MOZER MC, 1993, PREDICTING FUTURE UN NOLFI S, 1997, ADAPT BEHAV, V5, P343 NORDIN P, 1996, ADAPT BEHAV, V5, P107 OWEN C, 1996, 1 EUR WORKSH ADV MOB PEARLMUTTER BA, 1995, IEEE T NEURAL NETWOR, V6, P1212 PFEIFER R, 1998, ROBOTICS AUTONOMOUS, P2 PINEDA FJ, 1987, PHYS REV LETT, V59, P2229 RINKUS G, 1995, THESIS BOSTON U MASS SCHWENKER F, 1996, NEURAL NETWORKS, V9, P445 STEELS L, 1997, P 4 EUR C ART LIF, P473 TANI J, 1997, P 4 EUR C ART LIF, P309 THRUN S, 1996, EXPLANATION BASED NE TORRANCE MC, 1992, WORK NOT AAAI FALL S WILLSHAW DJ, 1969, NATURE, V222, P960 WYATT J, 1998, ROBOTICS AUTONOMOUS, V1, P41 YANCO H, 1993, ANIMALS ANIMATS, V2, P478 ZIMMER UR, 1996, NEUROCOMPUTING, V13, P247 ZREHEN S, 1995, THESIS SWISS FEDERAL N R P D P Y V L I S B P E P U T E R P T A U T I S O A B 47 WIN 1999 7 1 35 63 ISI:000080413000003 J Payne, BR Lomber, SG A method to assess the functional impact of cerebral connections on target JOURNAL OF NEUROSCIENCE METHODS We describe an innovative and tested approach combining two individually p simultaneously the functional impact of multiple projections on target popula The rationale is simple: silence a defined set of efferent projections from one deactivation and then measure the impact of the deactivation on activities in 2-deoxyglucose (2DG). This is a straightforward and sound approach because 2 levels of underlying neural activity. All distant modifications evoked by efferent projections are examined in anatomical tissue and simultaneously to provide a global view of the functional impacts of the set of projectio method, downward adjustments of 2DG uptake levels identify removals of net upward adjustments identify net removals of suppressive influences. Future po of the technique, including optical imaging, are discussed. Overall, the t provide fundamental, new measures on cerebral network interactions that both static models of cerebral networks and electrophysiological measures of fun neurons. (C) 1999 Elsevier Science B.V. All rights reserved. C BLASDEL GG, 1986, NATURE, V321, P579 R BONHOEFFER T, 1996, BRAIN MAPPING METHOD DEUEL RK, 1984, ANN NEUROL, V15, P521 FRIEDMAN HR, 1987, EXP BRAIN RES, V66, P543 FRIEDMAN HR, 1989, J NEUROSCI, V9, P4111 GEESAMAN BJ, 1997, CEREB CORTEX, V7, P749 GOLDMANRAKIC PS, 1988, ANNU REV NEUROSCI, V11, P137 GONZALEZLIMA F, 1992, ADV METABOLIC MAPPIN HOF PR, 1995, J COMP NEUROL, V362, P109 HOREL JA, 1991, METHODS NEUROSCIENCE HUPE JM, 1998, NATURE, V394, P784 HUPE JM, 1999, J NEUROSCI METH, V86, P129 JOUVE B, 1998, CEREB CORTEX, V8, P28 KOSSLYN SM, 1993, CEREB CORTEX, V3, P567 KUHL DE, 1980, ANN NEUROL, V8, P47 LOMBER SG, 1999, J NEUROSCI METH, V86, P179 LOWEL S, 1987, J COMP NEUROL, V255, P401 MAEKAWA H, 1993, NEUROSCI RES, V17, P315 MALPELI JG, 1999, J NEUROSCI METH, V86, P119 MARTIN WRW, 1983, ANN NEUROL, V14, P168 MAUNSELL JHR, 1990, J NEUROSCI, V10, P3323 MCINTOSH AR, 1992, ADV METABOLIC MAPPIN MCINTOSH AR, 1994, J NEUROSCI, V14, P655 MILNER AD, 1996, VISUAL BRAIN ACTION PAYNE B, 1997, TRENDS NEUROSCI, V20, P348 PAYNE BR, 1991, VISUAL NEUROSCI, V6, P283 PAYNE BR, 1996, P NATL ACAD SCI USA, V93, P290 PAYNE BR, 1996, TRENDS NEUROSCI, V19, P535 PAYNE BR, 1996, VISUAL NEUROSCI, V13, P805 REDIES C, 1990, J NEUROSCI, V10, P2791 ROSENQUIST AC, 1985, CEREB CORTEX, V3, P81 SCANNELL JW, 1995, J NEUROSCI, V15, P1463 SCANNELL JW, 1999, IN PRESS CEREB CORTE SOH K, 1978, ARCH NEUROL-CHICAGO, V35, P625 SOKOLOFF L, 1977, J NEUROCHEM, V44, P295 SOKOLOFF L, 1980, NEUROSCI RES PROG B, V19, P159 SOKOLOFF L, 1981, INT REV NEUROBIOL, V22, P287 SOMMER FT, 1997, TRENDS RES, P511 TAGAMETS MA, 1997, TRENDS RES, P949 TOOTELL RBH, 1988, J NEUROSCI, V8, P1500 TOOTELL RBH, 1988, J NEUROSCI, V8, P1531 VANDUFFEL W, 1995, J COMP NEUROL, V354, P161 VANDUFFEL W, 1997, EUR J NEUROSCI, V9, P1314 VANDUFFEL W, 1997, P NATL ACAD SCI USA, V94, P7617 VANDUFFEL W, 1998, MOL PSYCHIATR, V3, P215 VILLA AEP, 1999, J NEUROSCI METH, V86, P161 WHITE EL, 1989, CORTICAL CIRCUITS SY YAMASAKI DS, 1991, J NEUROPHYSIOL, V66, P651 YOUNG MP, 1992, NATURE, V358, P152 YOUNG MP, 1993, P ROY SOC LOND B BIO, V252, P13 YOUNG MP, 1994, REV NEUROSCI, V5, P227 YOUNG MP, 1995, PHILOS T ROY SOC B, V348, P281 N R P D P Y V L I S B P E P U T E R P T A U T I S O A B 52 JAN 1999 86 2 195 208 ISI:000078602500008 J Turner, M Austin, J Matching performance of binary correlation matrix memories NEURAL NETWORKS We introduce a theoretical framework for estimating the matching performance acting as hetero-associative memories. The framework is applicable to nonsystems with binary (0, I) Hebbian weights and hard-limited threshold. It ca matching of single or multiple data items in non-square memories. Theoretical a probability theory framework. Inherent uncertainties in the matching pro the use of probability distributions to describe the numbers of correct an during retrieval. Theoretical predictions are verified experimentally for used to aid in the design of larger systems. The results highlight the fact can act as highly efficient memories provided a small probability of retriev Elsevier Science Ltd. All rights reserved. C ABUMOSTAFA YS, 1985, IEEE T INFORM THEORY, V31, P461 R AMARI S, 1989, NEURAL NETWORKS, V2, P451 AMIT DJ, 1985, PHYS REV A, V32, P1007 ANDERSON JA, 1972, MATH BIOSCI, V14, P197 AUSTIN J, 1987, IMAGE VISION COMPUT, V5, P251 AUSTIN J, 1992, P INT C ART NEUR NET AUSTIN J, 1994, MICRONEURO AUSTIN J, 1994, P 4 INT C MICR NEUR, P58 AUSTIN J, 1995, INT J FUZZY SETS SYS, V82, P223 AUSTIN J, 1996, IEE C IM DAT BUCKINGHAM J, 1993, NETWORK-COMP NEURAL, V4, P441 GARDNERMEDWIN AR, 1976, P ROY SOC LOND B BIO, V194, P375 GIBSON WG, 1992, NEURAL NETWORKS, V5, P645 HEBB DO, 1949, ORG BEHAV HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 KOHONEN T, 1973, IEEE T COMPUTERS LITTLE WA, 1974, MATH BIOSCI, V19, P101 MARR D, 1971, PHILOS T ROY SOC B, V262, P23 NAKANO K, 1972, IEEE T SYST MAN CYB, V2, P68 PALM G, 1980, BIOL CYBERN, V36, P19 SCHWENKER F, 1996, NEURAL NETWORKS, V9, P445 STEINBUCH K, 1961, KYBERNETIK, V1, P36 WILLETT P, 1991, 3 DIMENSIONAL CHEM S WILLSHAW DJ, 1969, NATURE, V222, P960 N 24 R P DEC D P 1997 Y V 10 L I 9 S B 1637 P E 1648 P U ISI:000071201100009 T E R P T A U T I S O A B J Wennekers, T Palm, G On the relation between neural modelling and experimental neuroscience THEORY IN BIOSCIENCES This paper discusses the relation of theory and experiment in neuroscience exe often made in models of coherent activation in the cortex: basic feature-cod and global binding of whole objects. Apparently these assumptions are not experimental evidence. We propose that it is the single synchronized populati of feature-coding cells are temporally clustered in our opinion by recurre each burst a single stimulus is processed (if there are several). Synchroniza sites which physically interact. These principles are illustrated by compu C ABELES M, 1991, CORTICONICS R ANDERSON JA, 1988, NEUROCOMPUTING BIBBIG A, 1995, BEHAV BRAIN RES, V66, P169 BRAITENBERG V, 1991, ANATOMY CORTEX BULLIER J, 1993, CONCEPT NEUROSCI, V4, P159 BUSH PC, 1991, NEURAL COMPUT, V3, P19 CAMPBELL S, 1994, OSUCISRC894TR43 DEP DONG DW, 1995, NETWORK-COMP NEURAL, V6, P345 ECKHORN R, 1988, BIOL CYBERN, V60, P121 ECKHORN R, 1990, NEURAL COMPUT, V2, P293 ECKHORN R, 1993, EXP BRAIN RES, V95, P177 ECKHORN R, 1993, NEUROREPORT, V4, P243 ENGEL AK, 1991, SCIENCE, V252, P1177 FAHLE M, 1991, BIOL CYBERN, V66, P1 GERSTNER W, 1993, BIOL CYBERN, V68, P363 GERSTNER W, 1995, PHYS REV E, V51, P738 GRAY CM, 1989, NATURE, V338, P334 GRAY CM, 1989, P NATL ACAD SCI USA, V86, P1698 GRAY CM, 1994, J COMPUT NEUROSCI, V1, P11 HORN D, 1991, NEURAL COMPUT, V3, P31 KOCH C, 1992, NEURAL COMPUT, V4, P211 KONIG P, 1995, NEURAL COMPUT, V7, P469 KONIG P, 1995, P NATL ACAD SCI USA, V92, P290 MACGREGOR RJ, 1987, NEURAL BRAIN MODELIN NEVEN H, 1992, BIOL CYBERN, V67, P309 PALM G, 1982, CELL ASSEMBLIES GUID PALM G, 1990, CONCEPTS NEUROSCI, V1, P133 RITZ R, 1994, BIOL CYBERN, V71, P349 SALIN PA, 1995, PHYSIOL REV, V75, P107 SCHILLEN TB, 1994, BIOL CYBERN, V70, P397 SCHWENKER F, 1996, NEURAL NETWORKS, V9, P445 SHAW GL, 1988, BRAIN THEORY SINGER W, 1995, ANNU REV NEUROSCI, V18, P555 SOMPOLINSKY H, 1994, NEURAL COMPUT, V6, P642 TONONI G, 1992, CEREB CORTEX, V2, P310 VONDERMALSBURG C, 1986, BIOL CYBERN, V54, P29 VONDERMALSBURG C, 1992, BIOL CYBERN, V67, P233 WENNEKERS T, 1994, EUROP J NEUROSCI S, V7 WENNEKERS T, 1995, SUPERCOMPUTING BRAIN, P301 WILSON MA, 1991, NEURAL COMPUT, V3, P498 N R P D P Y V L I S B P E P U T E R P T A U T I S O A B 40 SEP 1997 116 3 267 283 ISI:A1997YA84800006 J Hirase, H Recce, M A search for the optimal thresholding sequence in an associative memory NETWORK-COMPUTATION IN NEURAL SYSTEMS In learning matrix associative memory networks, the choice of threshold value factors for determining the recall performance. Choice of threshold is especi recall, as each network state is dependent on the prior states. Recently, statistical approximation to formalize the dynamics of partially connected r formalism, we evaluate all of the possible thresholding sequences and find the highest storage capacity. The resulting optimal strategy can be closel that is proportional to the activation of the network plus an offset. The associative memory is shown to match well with the predictions of the theo to one of the simplest putative roles of interneurons which provide a line total unweighted input from the principal cells. C AMARI S, 1989, NEURAL NETWORKS, V2, P451 R BENNETT MR, 1994, PHILOS T ROY SOC B, V343, P167 BUCKINGHAM J, 1993, NETWORK-COMP NEURAL, V4, P441 BUCKINGHAM JT, 1991, THESIS U EDINBURGH FROLOV A, 1995, NETWORK-COMP NEURAL, V6, P513 FROLOV A, 1995, NETWORK-COMP NEURAL, V6, P535 GARDNERMEDWIN AR, 1976, P ROY SOC LOND B BIO, V194, P375 GARDNERMEDWIN AR, 1989, P ROY SOC LOND B BIO, V238, P137 GIBSON WG, 1992, NEURAL NETWORKS, V5, P645 GRAHAM B, 1995, BIOL CYBERN, V72, P337 HIRASE H, 1995, P INT C ART NEUR NET, P509 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V81, P6871 LANSNER A, 1985, IEEE T PATTERN ANAL, V7, P490 MARR D, 1971, PHILOS T ROY SOC B, V262, P23 MCNAUGHTON BL, 1987, TRENDS NEUROSCI, V10, P408 PALM G, 1992, NETWORK-COMP NEURAL, V3, P177 TREVES A, 1991, NETWORK, V2, P371 WILLSHAW DJ, 1969, NATURE, V222, P960 WILLSHAW DJ, 1990, PHILOS T ROY SOC B, V329, P205 N 19 R P NOV D P 1996 Y V 7 L I 4 S B 741 P E 756 P U ISI:A1996VY19700008 T E R P T A U T I S O J Tanaka, T Bit error probability of an associative memory with many-to-many correspon NETWORK-COMPUTATION IN NEURAL SYSTEMS A In associative memories (AMs) an important characteristic is the type of i B the connection weight. Conventional AM models store a one-to-one correspond In these models therefore, the cue pattern used to recall a pattern is rest and noisy versions of this associated pair. To overcome this restriction, a new model is proposed in which a many-to-ma patterns and content patterns is stored. A content pattern can be recalled fr which are sequentially provided to the network. The combination of key patterns to the AM and increases its usefulness. In this paper, the bit error probabil signal by each neuron is estimated. Computer simulations show good agreeme C AMARI S, 1972, IEEE T COMPUT, V21, P1197 R AMARI S, 1988, NEURAL NETWORKS, V1, P63 AMARI S, 1989, NEURAL NETWORKS, V2, P451 AMIT DJ, 1989, MODELING BRAIN FUNCT BUCKINGHAM J, 1993, NETWORK-COMP NEURAL, V4, P441 DOMANY E, 1989, J PHYS A-MATH GEN, V22, P2081 GIBSON WG, 1992, NEURAL NETWORKS, V5, P645 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 KOHONEN T, 1972, IEEE T COMPUT, V21, P353 KOHONEN T, 1989, SELF ORG ASS MEMORY MEUNIER C, 1991, NETWORK-COMP NEURAL, V2, P469 NAKANO K, 1972, IEEE T SYST MAN CYB, V2, P381 OKADA M, 1993, P INT JOINT C NEUR N, P2624 PALM G, 1992, NETWORK-COMP NEURAL, V3, P177 SHIINO M, 1993, PHYS REV E, V48, P867 SOMPOLINSKY H, 1986, PHYS REV LETT, V57, P2861 N 16 R P AUG D P 1996 Y V 7 L I 3 S B 573 P E 586 P U ISI:A1996VT25700007 T E R P J T A FROLOV, A U KARTASHOV, A GOLTSEV, A FOLK, R T QUALITY AND EFFICIENCY OF RETRIEVAL FOR WILLSHAW-LIKE AUTOASSOCIATIVE NETW I S NETWORK-COMPUTATION IN NEURAL SYSTEMS O A The informational properties of a neural network model of an autoassociative B synapses are investigated. The model is a modification of the Willshaw netw which keeps approximately constant the number of active neurons (winners) asymptotic case of large number of neurons, informational characteristics hav for single-step correction. Comparison with simulations shows that the max attains its asymptotic values for networks with surprisingly small number o for multistep correction show considerable improvement over the single-ste C AMARI S, 1989, NEURAL NETWORKS, V2, P451 R AMARI S, 1990, P IEEE, V78, P1443 AMIT DJ, 1987, ANN PHYS-NEW YORK, V173, P30 AMIT DJ, 1987, PHYS REV A, V35, P2293 AMOSOV NM, 1967, MODELING THINKING MI BUCKINGHAM J, 1992, NETWORK-COMP NEURAL, V3, P407 BUHMANN J, 1989, PHYS REV A, V39, P2689 DUNINBARKOWSKI WL, 1985, BIOL CYBERN, V51, P407 FROLOV A, 1995, NETWORK-COMP NEURAL, V6, P535 FROLOV AA, 1987, NEURAL MODELS ASSOCI FROLOV AA, 1988, INFORMATIONAL CHARAC FROLOV AA, 1989, BIOFIZIKA, V34, P868 FROLOV AA, 1993, NETWORK-COMP NEURAL, V4, P495 FROLOV AA, 1996, UNPUB NETWORK COMPUT GARDNER E, 1986, J PHYS A-MATH GEN, V19, L107 GARDNERMEDWIN AR, 1976, P ROY SOC LOND B BIO, V194, P375 GIBSON WG, 1992, NEURAL NETWORKS, V5, P645 HENKEL RD, 1991, J PHYS A-MATH GEN, V24, P2201 HOPFIELD JJ, 1982, P NATL ACAD SCI USA, V79, P2554 HORNER H, 1989, Z PHYS B CON MAT, V75, P133 HUSEK D, 1994, NEURAL NETWORK WORLD, V4, P53 KANTER I, 1988, PHYS REV A, V37, P2739 KIRKPATRICK S, 1978, PHYS REV B, V17, P4384 MIYASHITA Y, 1988, NATURE, V331, P68 NADAL JP, 1991, J PHYS A-MATH GEN, V24, P1093 PALM G, 1980, BIOL CYBERN, V36, P19 PALM G, 1992, NETWORK-COMP NEURAL, V3, P177 PEREZVICENTE CJ, 1989, J PHYS A, V22, P559 ROLLS ET, 1988, NEURAL MOL BASES LEA, P503 WILLSHAW D, 1971, THESIS EDINBURGH U WILLSHAW D, 1981, PARALLEL MODELS ASSO WILLSHAW DJ, 1969, NATURE, V222, P960 N R P D P Y V L I S B P E P U T E R 32 NOV 1995 6 4 513 534 ISI:A1995TH70600001 P J T A FROLOV, A U KARTASHOV, A GOLTSEV, A FOLK, R T QUALITY AND EFFICIENCY OF RETRIEVAL FOR WILLSHAW-LIKE AUTOASSOCIATIVE NETW I S NETWORK-COMPUTATION IN NEURAL SYSTEMS O A We study the recognition properties of a modification of the Willshaw asso B threshold analytically for the asymptotic case of large networks and singl of spurious stable states and the size of their attraction basins are calcul simulations for single- and multistep dynamics. The appropriateness of the on the remanent overlap is demonstrated. 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