  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.
