Title
Mean field and capacity in realistic networks of spiking neurons storing sparsely coded random memories.
Abstract
Mean-field (MF) theory is extended to realistic networks of spiking neurons storing in synaptic couplings of randomly chosen stimuli of a given low coding level. The underlying synaptic matrix is the result of a generic, slow, long-term synaptic plasticity of two-state synapses, upon repeated presentation of the fixed set of the stimuli to be stored. The neural populations subtending the MF description are classified by the number of stimuli to which their neurons are responsive (multiplicity). This involves 2p + 1 populations for a network storing p memories. The computational complexity of the MF description is then significantly reduced by observing that at low coding levels (f), only a few populations remain relevant: the population of mean multiplicity - pf and those of multiplicity of order square root pf around the mean. The theory is used to produce (predict) bifurcation diagrams (the onset of selective delay activity and the rates in its various stationary states) and to compute the storage capacity of the network (the maximal number of single items used in training for each of which the network can sustain a persistent, selective activity state). This is done in various regions of the space of constitutive parameters for the neurons and for the learning process. The capacity is computed in MF versus potentiation amplitude, ratio of potentiation to depression probability and coding level f. The MF results compare well with recordings of delay activity rate distributions in simulations of the underlying microscopic network of 10,000 neurons.
Year
DOI
Venue
2004
10.1162/0899766042321805
Neural Computation
Keywords
DocType
Volume
sparse coding,computational complexity,stationary state,mean field,synaptic plasticity,bifurcation diagram
Journal
16
Issue
ISSN
Citations 
12
0899-7667
13
PageRank 
References 
Authors
1.17
5
4
Name
Order
Citations
PageRank
Emanuele Curti1131.17
Gianluigi Mongillo2859.03
Giancarlo La Camera3385.05
Daniel J. Amit49223.21