title: Correlation between neural spike trains increases with firing rate.
abstract: Populations of neurons in the retina, olfactory system, visual and somatosensory thalamus, and several cortical regions show temporal correlation between the discharge times of their action potentials (spike trains). Correlated firing has been linked to stimulus encoding, attention, stimulus discrimination, and motor behaviour. Nevertheless, the mechanisms underlying correlated spiking are poorly understood, and its coding implications are still debated. It is not clear, for instance, whether correlations between the discharges of two neurons are determined solely by the correlation between their afferent currents, or whether they also depend on the mean and variance of the input. We addressed this question by computing the spike train correlation coefficient of unconnected pairs of in vitro cortical neurons receiving correlated inputs. Notably, even when the input correlation remained fixed, the spike train output correlation increased with the firing rate, but was largely independent of spike train variability. With a combination of analytical techniques and numerical simulations using 'integrate-and-fire' neuron models we show that this relationship between output correlation and firing rate is robust to input heterogeneities. Finally, this overlooked relationship is replicated by a standard threshold-linear model, demonstrating the universality of the result. This connection between the rate and correlation of spiking activity links two fundamental features of the neural code.
authors: Center for Neural Science, New York University, New York 10003, USA. jrocha@cns.nyu.edu
authors: Jaime
authors: J
authors: de la Rocha
authors: Brent
authors: B
authors: Doiron
authors: Eric
authors: E
authors: Shea-Brown
authors: Kresimir
authors: K
authors: Josić
authors: Alex
authors: A
authors: Reyes
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001303:Auditory Cortex / Q000166:cytology; D051379:Mice; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013003:Somatosensory Cortex / Q000166:cytology; D013997:Time Factors