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An Extended Model for Stimulus Onset Asynchrony (SOA) in Stroop
An Extended Model for Stimulus Onset Asynchrony (SOA) in Stroop

Ch19 Lecture
Ch19 Lecture

final scientific program
final scientific program

... One of the fundamental problems in neuroscience today is to understand how the activation of large populations of neurons gives rise to the higher order functions of the brain including learning, memory, cognition, perception, action and ultimately conscious awareness. Electrophysiological recording ...
An optical neural interface: in vivo control of rodent
An optical neural interface: in vivo control of rodent

... stimulation efficacy without an increased side effect profile. Employing light to activate neurons has emerged as an attractive new concept (for review, see [21–24]). Leveraging advances in chemical biology and molecular genetics, several groups have developed novel optical techniques to control neu ...
Review Early Steps in the Development of the Forebrain
Review Early Steps in the Development of the Forebrain

Computational models of reinforcement learning
Computational models of reinforcement learning

From spike frequency to free recall:
From spike frequency to free recall:

... anatomical data, without any distortion by verbal description. Linking these levels requires computational models which are constructed at a neural level within the constraints of physiological and anatomical data. Ultimately, linking these levels will require not only that the models of information ...
Sten Grillner
Sten Grillner

Consolidation of motor memory
Consolidation of motor memory

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PDF

Lateral olfactory processing
Lateral olfactory processing

... much less effective at eliminating the observed inhibition than the simultaneous blockade of both receptors at once. Interestingly, the magnitude of the lateral inhibition evoked by an odorant was wellcorrelated with the total activity that odorant evokes across all ORNs in the antennal lobe. This f ...
Causal networks as the backbone for temporal data-to-text
Causal networks as the backbone for temporal data-to-text

... handwritten rules will be further researched. In particular, multiple time series models, such as vector autoregressive processes and dynamic Bayesian networks, could possibly be used to discover causal relations types. They could be trained on available time series corpora and/or based on domain kn ...
ACQ_and_the_Basal_Ganglia
ACQ_and_the_Basal_Ganglia

An optical neural interface: in vivo control of
An optical neural interface: in vivo control of

Fixing Functionalism
Fixing Functionalism

... account is produced that violates intuition, it is sometimes the wise course to table the intuition rather than the account itself. Arguably, this is the path that physicists took with quantum mechanics, which has an elegant formalism but still has yet to fully explicate the conditions under which w ...
The neural basis of the speed–accuracy tradeoff - Eric
The neural basis of the speed–accuracy tradeoff - Eric

Learning Morphology by Itself1 - Mediterranean Morphology Meetings
Learning Morphology by Itself1 - Mediterranean Morphology Meetings

... phonologically weak, often unstressed, word boundary positions. Moreover, they convey fairly abstract and procedural semantic content (i.e. morpho-syntactic properties), having very few if any perceptual correlates in the grounding environment where words are uttered. Finally, when a language offers ...
Tutorial on Pattern Classification in Cell Recording
Tutorial on Pattern Classification in Cell Recording

ling411-11 - Rice University
ling411-11 - Rice University

... • are subwebs with many nodes each • have to be interconnected into a larger web • along with further web structure for conceptual information ...
A Comparison of Neural Spike Classification Techniques.
A Comparison of Neural Spike Classification Techniques.

Muscle networks: Connectivity analysis of EMG activity during postural control
Muscle networks: Connectivity analysis of EMG activity during postural control

... PDC takes into account the common input from other areas and is therefore a more direct reflection of coupling between two areas9. The optimal model order was determined for each condition and subject using Akaike’s information criterion and the average model order was 14.0 ± 3.7. In order to valida ...
Chemical Analogies: Two Kinds of Explanation
Chemical Analogies: Two Kinds of Explanation

... with weights equal to a parameter i are formed to connect all units that represent alternative mappings for the same element. In addition to the units representing mapping hypotheses, the network includes two special units. The semantic unit is used to convey information about the system's prior as ...
EN Sokolov`s Neural Model of Stimuli as Neuro
EN Sokolov`s Neural Model of Stimuli as Neuro

Art.-Schoenbaum (R) - UCSD Cognitive Science
Art.-Schoenbaum (R) - UCSD Cognitive Science

... activity during the delay, however, revealed that relatively few of these cells (24% or 18 of 74 in OFC and 25% or 11 of 44 in ABL) had similar selectivity during odor sampling (see Fig. 2 for the interval of analysis). Thus several subsets of cells encode information in the task, but the majority o ...
High-performance genetically targetable optical neural silencing by
High-performance genetically targetable optical neural silencing by

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Recurrent neural network

A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. This makes them applicable to tasks such as unsegmented connected handwriting recognition or speech recognition
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