Title
Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution.
Abstract
We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic processes stabilize the mean firing rate of the postsynaptic neuron, and (3) weak synapses adapt more slowly than strong ones, while maintenance of strong synapses is costly. Our results show that a synaptic update rule derived from these principles shares features, with spike-timing-dependent plasticity, is sensitive to correlations in the input and is useful for synaptic memory. Moreover, input selectivity (sharply tuned receptive fields) of postsynaptic neurons develops only if stimuli with strong features are presented. Sharply tuned neurons can coexist with unselective ones, and the distribution of synaptic weights can be unimodal or bimodal. The formulation of synaptic dynamics through an optimality criterion provides a simple graphical argument for the stability of synapses, necessary for synaptic memory.
Year
DOI
Venue
2007
10.1162/neco.2007.19.3.639
Neural Computation
Keywords
Field
DocType
unsupervised spike-timing-dependent plasticity,strong synapsis,basic principle,synaptic weight,optimality model,weight distribution,synaptic update rule,synaptic memory,principles shares feature,input selectivity,strong feature,postsynaptic neuron,synaptic dynamic,receptive field,hebbian learning,neural computation,computational neuroscience
Receptive field,Synaptic scaling,Synapse,Neuroscience,Postsynaptic potential,Hebbian theory,Synaptic plasticity,Spike-timing-dependent plasticity,Neural facilitation,Mathematics
Journal
Volume
Issue
ISSN
19
3
0899-7667
Citations 
PageRank 
References 
19
1.57
18
Authors
4
Name
Order
Citations
PageRank
Taro Toyoizumi117217.52
Jean-pascal Pfister215013.64
Kazuyuki Aihara31909333.03
Wulfram Gerstner42437410.08