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In the brain, information is encoded, transmitted and used to informbehaviour at the level of timing of action potentials distributed overpopulation of neurons. To implement neural-like systems in silico, to emulateneural function, and to interface successfully with the brain, neuromorphiccircuits need to encode information in a way compatible to that used bypopulations of neuron in the brain. To facilitate the cross-talk betweenneuromorphic engineering and neuroscience, in this Review we first criticallyexamine and summarize emerging recent findings about how population of neuronsencode and transmit information. We examine the effects on encoding and readoutof information for different features of neural population activity, namely thesparseness of neural representations, the heterogeneity of neural properties,the correlations among neurons, and the time scales (from short to long) atwhich neurons encode information and maintain it consistently over time.Finally, we critically elaborate on how these facts constrain the design ofinformation coding in neuromorphic circuits. We focus primarily on theimplications for designing neuromorphic circuits that communicate with thebrain, as in this case it is essential that artificial and biological neuronsuse compatible neural codes. However, we also discuss implications for thedesign of neuromorphic systems for implementation or emulation of neuralcomputation.

Constraints on the design of neuromorphic circuits set by the properties of neural population codes