Abstract
Neurons produce action potentials or spikes in response to a wide variety of inputs. Correlations between interspike intervals are often seen in data from single neurons, and are due to a combination of intrinsic mechanisms and the temporal properties of the input stimulus. Here we review recent progress in our understanding of how intrinsic correlations arise in simple biophysically justified neuron models. We further describe the generic conditions under which these correlations enhance the rate of transfer of information about time-varying stimuli. This work points to the importance of studying non-renewal first passage time problems in nonlinear dynamical systems.
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