Volume 13 Supplement 1

Twenty First Annual Computational Neuroscience Meeting: CNS*2012

Open Access

Inhibitory interneurons enable sparse code formation in a spiking circuit model of V1

BMC Neuroscience201213(Suppl 1):P148

DOI: 10.1186/1471-2202-13-S1-P148

Published: 16 July 2012

Sparse coding accounts for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields and the highly kurtotic firing rates of V1 neurons [1]. Current spiking network models of pattern learning [2] and sparse coding [3] require direct inhibitory connections between the excitatory simple cells, in violation of Dale's Law which states that neurons can either excite or inhibit but not both. At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained.

Here we show that adding a separate population of inhibitory neurons to a recently proposed model of V1 [3] not only brings spiking network models of sparse coding in line with Dale’s Law, but it also predicts excitatory-to-inhibitory neuron ratios and explains how inhibitory neurons may function computationally. When trained on natural images, this excitatory-inhibitory spiking circuit learns Gabor-like receptive fields as found in V1 using spiking neurons and synaptically local plasticity rules. The inhibitory cells enable sparse code formation using a novel learning rule by collaboratively discovering and suppressing correlations within the excitatory population (Figure 1). The model predicts that only a small number of inhibitory cells is required relative to excitatory cells, matching physiological ratios observed in primary visual cortex.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2202-13-S1-P148/MediaObjects/12868_2012_Article_2685_Fig1_HTML.jpg
Figure 1

A. Circuit diagram of our spiking network with separate excitatory (E) and inhibitory (I) neural populations (top) compared to current single population models (bottom). This network was simulated with different numbers of excitatory and inhibitory cells. B. Adding inhibitory cells to the network differentiates the receptive fields and decreases image reconstruction error during learning. C. This error reduction is caused by decreased correlations among the excitatory neurons that are collaborating to form a sparse representation of the visual input. The network was trained on 8x8 image patches (64 pixels) drawn from whitened natural images. Excitatory neuron counts (# E cells) ranged from 64 to 384 (1x to 6x overcomplete). Inhibitory neuron counts (# I cells) ranged from 3 to 64 (.05x to 1x overcomplete). We find that reconstruction errors are roughly constant for populations of interneurons that are at least ~20% of the size of the total population, assuming the total neural population is at least 4x overcomplete relative to the input. This is consistent with the 80/20 ratio of excitatory-to-inhibitory neurons observed in visual cortex.

Authors’ Affiliations

(1)
Redwood Center for Theoretical Neuroscience, University of California
(2)
Helen Wills Neuroscience Institute, University of California
(3)
Department of Physics, University of California

References

  1. Olshausen BA, Field DJ: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 1996, 381: 607-609. 10.1038/381607a0.View ArticlePubMedGoogle Scholar
  2. Masquelier T, Guyonneau R, Thorpe SJ: Competitive STDP-Based Spike Pattern Learning. Neural Comput. 2009, 21: 1259-1279. 10.1162/neco.2008.06-08-804.View ArticlePubMedGoogle Scholar
  3. Zylberberg J, Murphy JT, DeWeese MR: A Sparse Coding Model with Synaptically Local Plasticity and Spiking Neurons Can Account for the Diverse Shapes of V1 Simple Cell Receptive Fields. PLoS Comput Biol. 2011, 7 (10): e1002250-10.1371/journal.pcbi.1002250. doi:10.1371/journal.pcbi.1002250PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© King et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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