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NEUROCOMPUTING 2002年 44卷 715-720页

作者： Zhang, ZH Ballard, DH Univ Rochester Dept Comp Sci Rochester NY 14623 USA

Data implying that neurons can communicate with synchronous volleys are difficult to reconcile with the bulk of single unit recordings which do not show synchrony yet reveal substantial correlations with animal behavior. Our simulations reconcile these two sets of results by sharing a synchronous signal among groups of neurons in a way that the average signal through any particular neuron exhibits conventional receptive field properties. The simulation models a subset of the connections between the LGN and VI and shows that synchronous computation at a high firing rate can appear at an individual cell as random spikes at a lower rate. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： distributed synchrony predictive coding probabilistic firing sparse coding minimum description length (MDL)

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distributed synchrony in an attractor of spiking neurons

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NEUROCOMPUTING 2000年 32卷 409-414页

作者： Horn, D Levy, N Ruppin, E Tel Aviv Univ Raymond & Beverly Sackler Fac Exact Sci Sch Phys & Astron IL-69978 Tel Aviv Israel Tel Aviv Univ Sch Math Sci IL-69978 Tel Aviv Israel

Recently, it has been shown that there exists a temporal synaptic learning curve, composed of potentiation;for short time delays between pre- and post-synaptic neuronal spiking, and depression for spiking events occurring in the opposite order. We investigate the effect that such a curve can have on the temporal realization of an attractor in an associative memory network of spiking neurons. We find that an interesting result of this learning paradigm is the formation of distributed synchrony, i.e. spontaneous division of the Hebbian cell assembly into groups of cells that fire in a cyclic manner. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： distributed synchrony spiking neurons Hebbian cell assembly temporal synaptic learning curve

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A single spike model of predictive coding

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NEUROCOMPUTING 2004年 58卷 165-171页

作者： Zhang, ZH Ballard, DH Univ Rochester Dept Comp Sci Rochester NY 14627 USA

Current models of cortical computation are based on analog quantities instead of single spikes. This paper extends the predictive coding model (Nature Neurosci. 2(1) (1999) 79) to the level of neural signaling. Neurons in our model use a mixed strategy to transmit information. Spikes are not only messages of computation, but also carriers of information with analog quantities encoded in their phases. Computation is shared among cells both in time and in space, such that information is signaled probabilistically in a distributed synchronous fashion. Contrary to "noise other than signal" interpretation of irregularity of neural signaling, our model proposes a computational role of such variability. (C) 2004 Published by Elsevier B.V.

关键词： distributed synchrony predictive coding probabilistic firing sparse coding minimum description length

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