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Neural Comput. 2019 Feb;31(2):270-311. doi: 10.1162/neco_a_01158. Epub 2018 Dec 21.

Functional Diversity in the Retina Improves the Population Code.

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Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, U.S.A.
Department of Physics, Ecole Normale Supérieure, 75005 Paris, France
Department of Physics, Princeton University, Princeton, NJ 08544, U.S.A.
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, U.S.A.; Department of Physics, Ecole Normale Supérieure, 75005 Paris; Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research University, 75231 Paris; Université Paris Diderot Sorbonne Paris Cité, 75031 Paris; Sorbonne Universités UPMC Université Paris 6, 75005 Paris, France; CNRS


Within a given brain region, individual neurons exhibit a wide variety of different feature selectivities. Here, we investigated the impact of this extensive functional diversity on the population neural code. Our approach was to build optimal decoders to discriminate among stimuli using the spiking output of a real, measured neural population and compare its performance against a matched, homogeneous neural population with the same number of cells and spikes. Analyzing large populations of retinal ganglion cells, we found that the real, heterogeneous population can yield a discrimination error lower than the homogeneous population by several orders of magnitude and consequently can encode much more visual information. This effect increases with population size and with graded degrees of heterogeneity. We complemented these results with an analysis of coding based on the Chernoff distance, as well as derivations of inequalities on coding in certain limits, from which we can conclude that the beneficial effect of heterogeneity occurs over a broad set of conditions. Together, our results indicate that the presence of functional diversity in neural populations can enhance their coding fidelity appreciably. A noteworthy outcome of our study is that this effect can be extremely strong and should be taken into account when investigating design principles for neural circuits.

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