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Efficient codes and balanced networks

Abstract

Recent years have seen a growing interest in inhibitory interneurons and their circuits. A striking property of cortical inhibition is how tightly it balances excitation. Inhibitory currents not only match excitatory currents on average, but track them on a millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. We review, together with experimental evidence, recent theoretical approaches that investigate the advantages of such tight balance for coding and computation. These studies suggest a possible revision of the dominant view that neurons represent information with firing rates corrupted by Poisson noise. Instead, tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of magnitude more precise than a Poisson rate code. Moreover, tight balance may provide a template that allows cortical neurons to construct high-dimensional population codes and learn complex functions of their inputs.

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Figure 1: The conundrum of Poisson rate codes and E/I balance.
Figure 2: Schematic illustration of key experimental findings.
Figure 3: Coding with tightly balanced networks.
Figure 4: Computations with tightly balanced networks.

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Acknowledgements

We thank A. Renart and B. Atallah for discussions.

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Denève, S., Machens, C. Efficient codes and balanced networks. Nat Neurosci 19, 375–382 (2016). https://doi.org/10.1038/nn.4243

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