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Spike pattern distributions in model cortical networks
Stockholm University, Faculty of Science, Department of Mathematics. Matematisk statistik.
2008 (English)In: COSYNE-Computational and Systems Neuroscience 2008, Salt Lake City, 2008Conference paper (Other (popular science, discussion, etc.))
Abstract [en]

We can learn something about coding in large populations of neurons from models of the spike pattern distributions constructed from data. In our work, we do this for data generated from computational models of local cortical networks. This permits us to explore how features of the neuronal and synaptic properties of the network are related to those of the spike pattern distribution model. We employ the approach of Schneidman et al [1] and model this distribution by a Sherrington-Kirkpatrick (SK) model: P[S] = Z-1exp(½ΣijJijSiSj+ΣihiSi). In the work reported here, we analyze spike records from a simple model of a cortical column in a high-conductance state for two different cases: one with stationary tonic firing and the other with a rapidly time-varying input that produces rapid variations in firing rates. The average cross-correlation coefficient in the former is an order of magnitude smaller than that in the latter.

To estimate the parameters Jij and hi we use a technique [2] based on inversion of the Thouless-Anderson-Palmer equations from spin glass theory. We have performed these fits for groups of neurons of sizes from 12 to 200 for tonic firing and from 6 to 800 for the case of the rapidly time-varying “stimulus”. The first two figures show that the distributions of Jij’s in the two cases are quite similar, both growing slightly narrower with increasing N. They are also qualitatively similar to those found by Schneidman et al and by Tkačik et al [3] for data from retinal networks. As in their work, it does not appear to be necessary to include higher order couplings. The means, which are much smaller than the standard deviations, also decrease with N, and the one for tonic firing is less than half that for the stimulus-driven network.

However, the models obtained never appear to be in a spin glass phase for any of the sizes studied, in contrast to the finding of Tkačik et al, who reported spin glass behaviour at N=120. This is shown in the third figure panel. The x axis is 1/J, where J = N1/2std(Jij) and the y axis is H/J, where H is the total “field” N-1Σi(hi+ΣjJij‹Sj›). The green curve marks the Almeida-Thouless line separating the normal and spin glass phases in this parameter plane. All our data, for N ≤800 (the number of excitatory neurons in the originally-simulated network), lie in the normal region, and extrapolation from our results predicts spin glass behaviour only for N>5000.

[1] E. Schneidman et al., Nature 440 1007-1012 (2006)

[2] T. Tanaka, Phys Rev E 58 2302-2310 (1998); H. J. Kappen and F. B Rodriguez, Neural Comp 10 1137-1156 (1998)

[3] G. Tkačik et al., arXiv:q-bio.NC/0611072 v1 (2006)

Place, publisher, year, edition, pages
National Category
Neurosciences Bioinformatics and Systems Biology Probability Theory and Statistics
URN: urn:nbn:se:su:diva-16406PubMedID: diva2:182926
Available from: 2008-12-17 Created: 2008-12-17Bibliographically approved

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