Modeling the mammalian visual system
Methods in neuronal modeling
Elements of information theory
Elements of information theory
Chaotic balanced state in a model of cortical circuits
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Distortion of Neural Signals by Spike Coding
Neural Computation
Discrimination with spike times and isi distributions
Neural Computation
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It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance.