  Advances in neural recording methods enable sampling from populations of
thousands of neurons during the performance of behavioral tasks, raising the
question of how recorded activity relates to the theoretical models of
computations underlying performance. In the context of decision making in
rodents, patterns of functional connectivity between choice-selective cortical
neurons, as well as broadly distributed choice information in both excitatory
and inhibitory populations, were recently reported [1]. The straightforward
interpretation of these data suggests a mechanism relying on specific patterns
of anatomical connectivity to achieve selective pools of inhibitory as well as
excitatory neurons. We investigate an alternative mechanism for the emergence
of these experimental observations using a computational approach. We find that
a randomly connected network of excitatory and inhibitory neurons generates
single-cell selectivity, patterns of pairwise correlations, and
indistinguishable excitatory and inhibitory readout weight distributions, as
observed in recorded neural populations. Further, we make the readily
verifiable experimental predictions that, for this type of evidence
accumulation task, there are no anatomically defined sub-populations of neurons
representing choice, and that choice preference of a particular neuron changes
with the details of the task. This work suggests that distributed stimulus
selectivity and patterns of functional organization in population codes could
be emergent properties of randomly connected networks.
