Correlated firing and oscillations in spiking networks with global delayed inhibition

Authors:
Jin-li Xie;Zhi-Jie Wang;Andre Longtin
Affiliations:
College of Information Science and Technology, Donghua University, Shanghai 201620, China;College of Information Science and Technology, Donghua University, Shanghai 201620, China;Department of Physics and Center for Neural Dynamics, University of Ottawa, 150 Louis Pasteur, Ottawa, Ont., Canada K1N 6N5
Venue:
Neurocomputing
Year:
2012

Citing 7
Cited 1

Synchronization in networks of excitatory and inhibitory Neurons with sparse, random connectivity

Neural Computation
Spike Count Correlation Increases with Length of Time Interval in the Presence of Trial-to-Trial Variation

Neural Computation
Input-Driven Oscillations in Networks with Excitatory and Inhibitory Neurons with Dynamic Synapses

Neural Computation
Dependence of neuronal correlations on filter characteristics and marginal spike train statistics

Neural Computation
Correlations and population dynamics in cortical networks

Neural Computation
Stimulus-dependent correlations and population codes

Neural Computation
Cross-correlations in high-conductance states of a model cortical network

Neural Computation

Effect of nonidentical signal phases on signal amplification of two coupled excitable neurons

Neurocomputing

Quantified Score

Hi-index	0.02

Visualization

Abstract

In feed-forward networks, output pairwise correlations can increase with firing rate. Here, we study correlations between sensory neurons with global inhibitory feedback and cross-correlated external inputs. The average pairwise correlation coefficient is computed from simulations of a network of noisy leaky integrate-and-fire neurons with delayed spike-driven feedback. We focus on the relation between the correlation and the feedback strength. This relation is monotonically increasing when the common noise is frozen, and non-monotonic when it varies across trials. In both cases, beyond a certain feedback strength, the increase in correlation mirrors the emergence of asynchronous network oscillations quantified by the sharpness (''coherence'') of the peak in the spike train power spectral density. Our results suggest that pairwise correlations are strongly controlled by feedback via the interplay of mean firing rate and oscillatory activity. For frozen common noise, correlations in fact remain near zero up until oscillatory activity is sufficiently coherent. These results are found in both sub- and supra-threshold dynamic regimes, for low and moderate internal noise levels, as well as for a heterogeneous distribution of feedback gains or firing thresholds.