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Gating of Sensory Input by Spontaneous Cortical Activity
Gating of Sensory Input by Spontaneous Cortical Activity

... (CCG), AMUA is the area of peak of the MUA autocorrelogram, N1 is number of spikes of the analyzed single neuron, and NMUA is number of spikes in MUA. F lies in the range 0 to 1, with 0 indicating complete independence of the individual neuron from MUA and 1 indicating 100% time locking, i.e., that ...
Mind Design II : Philosophy, Psychology, Artificial Intelligence
Mind Design II : Philosophy, Psychology, Artificial Intelligence

... has to exist; X cannot be inside of Y, or indeed be related to Y in any other way, if Y does not exist. This is true of relations quite generally; but it is not true of intentionality. I can perfectly well imagine a party on a certain date, and also have beliefs, desires, and fears about it, even th ...
Mind Design II : Philosophy, Psychology, Artificial Intelligence
Mind Design II : Philosophy, Psychology, Artificial Intelligence

... has to exist; X cannot be inside of Y, or indeed be related to Y in any other way, if Y does not exist. This is true of relations quite generally; but it is not true of intentionality. I can perfectly well imagine a party on a certain date, and also have beliefs, desires, and fears about it, even th ...
Representation of Events in Nerve Nets and Finite Automata
Representation of Events in Nerve Nets and Finite Automata

... at time £; the 0 in the first row and second column that Na did ...
Multimodal Integration in Rostral Fastigial Nucleus Provides an
Multimodal Integration in Rostral Fastigial Nucleus Provides an

... After a neuron’s response to eye and head motion had been characterized, we next investigated its sensitivity to neck proprioceptive inputs using four different approaches. First, the monkey’s head was held stationary relative to the earth while its body was sinusoidally (1 Hz, ⫾40°/s) rotated below ...
Neurally Plausible Model of Robot Reaching Inspired by Infant
Neurally Plausible Model of Robot Reaching Inspired by Infant

... Competition among excited neurons in map C leads to firing of neuron r0 , which represents the new motor-sensory state. . . . . . . . . . . . ...
Pardis, a Fuzzy Extension to Multi agent Simulation Systems
Pardis, a Fuzzy Extension to Multi agent Simulation Systems

... the knowledge is represented and stored as qualitative data, and not quantities. In the recent years, Zadeh has introduced a new concept that is called “Computing with Words” (CW) [4]. The main idea behind this concept, which he believes is the next step in the natural evolution of fuzzy systems, is ...
FLP Semantics Without Circular Justifications for General Logic
FLP Semantics Without Circular Justifications for General Logic

Balanced Excitatory and Inhibitory Inputs to Cortical Neurons
Balanced Excitatory and Inhibitory Inputs to Cortical Neurons

Mirror Neuron System in Monkey: A Computational Modeling
Mirror Neuron System in Monkey: A Computational Modeling

... existing FARS (Fagg-Arbib-Rizzolatti-Sakata) model represents circuitry for visually-guided grasping of objects, linking parietal area AIP with F5 canonical neurons. The MNS1 model extends the AIP visual pathway by also modeling pathways, directed toward F5 mirror neurons, which match arm-hand traje ...
local connectivity between neurons of the rat globus pallidus
local connectivity between neurons of the rat globus pallidus

Dynamics and Synchronization of Motifs of Neuronal Populations in the Presence
Dynamics and Synchronization of Motifs of Neuronal Populations in the Presence

A Knowledge Representation Tool for Autonomous Machine
A Knowledge Representation Tool for Autonomous Machine

... concepts, which provides a denotational mathematic means for knowledge system representation and manipulation [18], [19], [20]. Concept algebra consists of three parts known as the mathematical model of abstract concepts, relational operations, and compositional operations. An abstract concept c is ...
Mesodermal and neuronal retinoids regulate the induction and
Mesodermal and neuronal retinoids regulate the induction and

... have been stymied by the early lethality of Raldh2 null embryos prior to motor neuron generation (Niederreither et al., 1999). A recent study where Raldh2 expression was genetically ablated in forelimb spinal motor neurons led to a decrease of forelimb LMCl neurons, supporting the idea that motor ne ...
Hippocampal mechanisms for the context-dependent retrieval of episodes 2005 Special issue
Hippocampal mechanisms for the context-dependent retrieval of episodes 2005 Special issue

Duration Tuning across Vertebrates
Duration Tuning across Vertebrates

... (medial) superior olivary complex (Guinan et al., 1972; Grothe et al., 1997, 2001), where dense excitatory projections lead into the ICc (Oliver et al., 1995). GABAergic offset responding neurons in the superior paraolivary complex are not candidates for providing offset-evoked excitatory input to t ...
Dynamics of sensory processing in the dual olfactory pathway of the
Dynamics of sensory processing in the dual olfactory pathway of the

Music Classification Using Significant Repeating Patterns
Music Classification Using Significant Repeating Patterns

... As the amount of music data increases, classification of music data has become an important issue. In [2][6], the machine learning techniques including naïve Bayesian, linear, and neural network are employed to build classifiers for music styles. As a result, they identify emotional classes of music ...
Music Classification Using Significant Repeating Patterns
Music Classification Using Significant Repeating Patterns

... As the amount of music data increases, classification of music data has become an important issue. In [2][6], the machine learning techniques including naïve Bayesian, linear, and neural network are employed to build classifiers for music styles. As a result, they identify emotional classes of music ...
(Statistical) Relational Learning
(Statistical) Relational Learning

Variational Inference for Dirichlet Process Mixtures
Variational Inference for Dirichlet Process Mixtures

EXPRESSIVITY-AWARE TEMPO TRANSFORMATIONS OF MUSIC PERFORMANCES USING CASE BASED REASONING
EXPRESSIVITY-AWARE TEMPO TRANSFORMATIONS OF MUSIC PERFORMANCES USING CASE BASED REASONING

... This dissertation is about expressivity-aware tempo transformations of monophonic audio recordings of saxophone jazz performances. It is a contribution to content-based audio processing, a field of technology that has recently emerged as an answer to the increased need to deal intelligently with the ...
ficient mice: Distinct effects in Finnish variant late infantile NCL
ficient mice: Distinct effects in Finnish variant late infantile NCL

... cortical subfields, respectively and display pronounced synaptic pathology in Cathepsin D deficient mice (Partanen et al., 2008). Immunohistochemical staining for these proteins revealed pronounced changes in the intensity and distribution of staining for these markers in the thalamocortical system of ...
Towards a Computational Model of Analogical Arguments
Towards a Computational Model of Analogical Arguments

Here - Institute of Cognitive Neuroscience
Here - Institute of Cognitive Neuroscience

... cells” fire selectively for an animal’s location in space (e.g., O’Keefe, 1976), but show little dependence on the animal’s orientation during random, open field foraging. We refer to this representation as allocentric, representing location relative to the environment, even though the location repr ...
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Neural modeling fields

Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
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