  The correlated variability in the responses of a neural population to the
repeated presentation of a sensory stimulus is a universally observed
phenomenon. Such correlations have been studied in much detail, both with
respect to their mechanistic origin and to their influence on stimulus
discrimination and on the performance of population codes. In particular,
recurrent neural network models have been used to understand the origin (or
lack) of correlations in neural activity. Here, we apply a model of recurrently
connected stochastic neurons to interpret correlations found in a population of
neurons recorded from mouse auditory cortex. We study the consequences of
recurrent connections on the stimulus dependence of correlations, and we
compare them to those from alternative sources of correlated variability, like
correlated gain fluctuations and common input in feed-forward architectures. We
find that a recurrent network model with random effective connections
reproduces observed statistics, like the relation between noise and signal
correlations in the data, in a natural way. In the model, we can analyze
directly the relation between network parameters, correlations, and how well
pairs of stimuli can be discriminated based on population activity. In this
way, we can relate circuit parameters to information processing.
