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

TL;DR

This paper reviews how neural population coding influences the design of neuromorphic circuits, emphasizing the importance of encoding compatibility with biological neural systems for effective communication and emulation.

Contribution

It critically examines recent findings on neural population encoding and discusses how these insights constrain neuromorphic circuit design for brain interfacing.

Findings

Neural encoding involves sparseness, heterogeneity, and correlations among neurons.

Encoding time scales affect information transmission and stability.

Design constraints are derived for neuromorphic circuits to match biological neural codes.

Abstract

In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this Review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the time scales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation.