Adaptive neural information processing with dynamical electrical synapses

The present study investigates a potential computational role of dynamical electrical synapses in neural information process. Compared with chemical synapses, electrical synapses are more efficient in modulating the concerted activity of neurons. Based on the experimental data, we propose a phenomenological model for short-term facilitation of electrical synapses. The model satisfactorily reproduces the phenomenon that the neuronal correlation increases although the neuronal firing rates attenuate during the luminance adaptation. We explore how the stimulus information is encoded in parallel by firing rates and correlated activity of neurons, and find that dynamical electrical synapses mediate a transition from the firing rate code to the correlation one during the luminance adaptation. The latter encodes the stimulus information by using the concerted, but lower neuronal firing rate, and hence is economically more efficient.