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Common terms and phrasesactivity amacrine amacrine cells Arbib array Backpropagation basal ganglia boolean Canvas chapter charString Code Segment columns command computes connection corresponding cortex CorticalArraySize cycle default defined described dimension display dynamics element epoch equation example excitatory executes feature detector fixation float frog function ganglion cells graph Hopfield Hopfield network implemented inhibition inhibitory initial initModule initRun input layer input pattern input port instantiation Java learning mapping matrix MaxSelector MaxSelectorModel membrane potential model layer module motor program selection neural network neurons nsl set NSLC NslDinFloat2 NSLM nslModule NslOutFrame object types Options output layer output port parameters private NslFloatO program selection units protocol public void simRun receptive fields recognition response retina saccade Schematic Capture Schematic Editor script window shown in figure simRun method simTrain simulation specified stimulus Superior Colliculus Table target thalamus threshold variable vector visual voting units weights Popular passagesPage 421 - C (1992) Efferent connections of the centromedian and parafascicular thalamic nuclei in the squirrel monkey: a PHA-L study of subcortical projections. J Comp Neurol 315:137-159 125. Page 418 - Gerfen, CR (1992). The neostriatal mosaic: Multiple levels of compartmental organization in the basal ganglia. Page 173 - ... (This use of the plausible hypothesis that our visual world is made up of relatively few connected regions... Page 178 - Further, the systems are so coupled that a point in the accommodation field M will excite the corresponding point in the disparity field S, and vice versa. Thus a high confidence in a particular (direction, depth) coordinate in one layer will bias activity in the other layer accordingly. The result is that the system will converge to a state affected by both types of information — although the monocular system can, by itself, yield depth estimates. S is... Page 178 - ... neighbors on the other eye in a systematic fashion, then the cooperative effect can swamp out the correct pairing and lead to the perception of the fence at an incorrect depth. In animals with frontal facing eyes such ambiguity can be reduced by the use of vergence information to drive the system with an initial depth estimate. Another method is to use accommodation information to provide the initial bias for a depth perception system; this is more appropriate to the amphibian, with its lateral-facing... Page 228 - With these problems in mind, we have sought distributed representations in which a single pattern (or task) is coded by a small subset of the units in the network. Although different subsets of units are allowed to overlap to a certain degree, interference between two patterns is minimized by the non-overlapping components. Inspired by the cell activities observed by Mitz... Page 252 - Rather than requiring that the network settle down into a stable state, the first unit that achieves a membrane potential above the threshold is declared the winner. In the case that more than one unit activated at the same instant, the standard winner-take-all circuit is used to squelch the activity of all but one. Using this particular algorithm allows for a faster simulation, since more time is required if the units must settle down to equilibrium. During the testing/learning trials, a pattern... Page 320 - If the chopsticks are placed the same distance apart, so that the gaps have the same width, and the barrier is 20 cm wide, then the naive frog tends to go for the gap in the direction of the prey (this was the case for 88% of the trials). The frog starts out approaching the fence trying to make its way through the gaps. During the first trials with the 20 cm barrier the frog goes straight towards the prey thus bumping into the barrier. When the frog is not able to go through a gap towards the prey... Page 34 - WN.i (n) is the value of a weight from neuron p in the hidden layer to neuron q in the output layer at step n • k indicates that the weight is associated with its destination layer. Page 229 - As shown in figure 8.35, the votes from each column are collected by the motor program selection units, labeled left, right, down, and no-go. References to this bookFrom Google ScholarSome Insights Into Computational Models of (Patho) Physiological ...PIOTR SUFFCZYNSKI, FABRICE WENDLING, JEAN-JACQUES BELLANGER, FERNANDO H LOPES DA SILVA - 2006 - PROCEEDINGS OF THE IEEE Miro: An Embedded Distributed Architecture for Biologically ...Alfredo Weitzenfeld, Sebastian Gutierrez-Nolasco, Nalini Venkatasubramanian - Retina Visual Input Compensation using the Crowley-Arbib Saccade ModelFortunato Flores Ando, Alfredo Weitzenfeld Ridel A Brain-Like Neural Network for Periodicity AnalysisKyriakos Voutsas, Gerald Langner, Jürgen Adamy, Michael Ochse - 2005 - IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS References from web pagesThe Neural Simulation Language - The MIT Press Weitzenfeld A., maArbib, A.Alexander. Нервный Язык Моделирования ... Acrobat Distiller, Job 50 ingentaconnect The Neural simulation language: A system for brain ... From schemas to neural networks [Paper] MIRO: A Distributed Embedded Architecture for Visually ... Neural Simulation Language The Neural Simulation Language: A System for Brain Modeling | Free ... Weitzenfeld A., maArbib, A.Alexander. The Neural Simulation ... Livros - THE NEURAL SIMULATION LANGUAGE / Alexander, Amanda;Arbib ... Bibliographic information
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