Topic: chaotic network fluctuations

Information encoding in neural circuits depends on how well time-varying stimuli are encoded by neural populations. Slow neuronal timescales, noise, and network chaos can compromise reliable and rapid population response to external stimuli. A dynamic balance of externally incoming currents by strong recurrent inhibition was previously proposed as a mechanism to accurately and robustly encode a time-varying stimulus in balanced networks of binary neurons, but a theory for recurrent rate networks was missing. Here, we develop a non-stationary dynamic mean-field theory that transparently expla