The continuum of operating modes for a passive model neuron
Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Integrate-and-fire neurons driven by correlated stochastic input
Neural Computation
Analysis of Fluctuation-Induced Firing in the Presence of Inhibition
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Proposed mechanisms for coincidence detection in the auditory brainstem
Biological Cybernetics
Cortical cells should fire regularly, but do not
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
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
Journal of Computational Neuroscience
Estimation of time-dependent input from neuronal membrane potential
Neural Computation
Does high firing irregularity enhance learning?
Neural Computation
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In this letter, we aim to measure the relative contribution of coincidence detection and temporal integration to the firing of spikes of a simple neuron model. To this end, we develop a method to infer the degree of synchrony in an ensemble of neurons whose firing drives a single postsynaptic cell. This is accomplished by studying the effects of synchronous inputs on the membrane potential slope of the neuron and estimating the degree of response-relevant input synchrony, which determines the neuron's operational mode. The measure is calculated using the normalized slope of the membrane potential prior to the spikes fired by a neuron, and we demonstrate that it is able to distinguish between the two operational modes. By applying this measure to the membrane potential time course of a leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular firing at high rates, we show that the partial reset model operates as a temporal integrator of incoming excitatory postsynaptic potentials and that coincidence detection is not necessary for producing such high irregular firing.