The effect of synchronized inputs at the single neuron level
Neural Computation
Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model
Neural Computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Self-organizing dual coding based on spike-time-dependent plasticity
Neural Computation
The high-conductance state of cortical networks
Neural Computation
Can spike coordination be differentiated from rate covariation?
Neural Computation
Impact of Higher-Order Correlations on Coincidence Distributions of Massively Parallel Data
Dynamic Brain - from Neural Spikes to Behaviors
Generation of correlated spike trains
Neural Computation
Stimulus-dependent correlations in threshold-crossing spiking neurons
Neural Computation
Efficient identification of assembly neurons within massively parallel spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Conditional mixture model for correlated neuronal spikes
Neural Computation
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
Journal of Computational Neuroscience
Mechanisms that modulate the transfer of spiking correlations
Neural Computation
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
Fuzzy frequent pattern mining in spike trains
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Impact of correlated neural activity on decision-making performance
Neural Computation
A new method to infer higher-order spike correlations from membrane potentials
Journal of Computational Neuroscience
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Pairwise correlations among spike trains recorded in vivo have been frequently reported. It has been argued that correlated activity could play an important role in the brain, because it efficiently modulates the response of a postsynaptic neuron. We show here that a neuron's output firing rate critically depends on the higher-order statistics of the input ensemble. We constructed two statistical models of populations of spiking neurons that fired with the same rates and had identical pairwise correlations, but differed with regard to the higher-order interactions within the population. The first ensemble was characterized by clusters of spikes synchronized over the whole population. In the second ensemble, the size of spike clusters was, on average, proportional to the pairwise correlation. For both input models, we assessed the role of the size of the population, the firing rate, and the pairwise correlation on the output rate of two simple model neurons: a continuous firing-rate model and a conductance-based leaky integrate-and-fire neuron. An approximation to the mean output rate of the firing-rate neuron could be derived analytically with the help of shot noise theory. Interestingly, the essential features of the mean response of the two neuron models were similar. For both neuron models, the three input parameters played radically different roles with respect to the postsynaptic firing rate, depending on the interaction structure of the input. For instance, in the case of an ensemble with small and distributed spike clusters, the output firing rate was efficiently controlled by the size of the input population. In addition to the interaction structure, the ratio of inhibition to excitation was found to strongly modulate the effect of correlation on the postsynaptic firing rate.