Abstract
Information about time-dependent sensory stimuli is encoded by the spike trains of neurons. Here we consider a population of uncoupled but noisy neurons (each subject to some intrinsic noise) that are driven by a common broadband signal. We ask specifically how much information is encoded in the synchronous activity of the population and how this information transfer is distributed with respect to frequency bands. In order to obtain some insight into the mechanism of information filtering effects found previously in the literature, we develop a mathematical framework to calculate the coherence of the synchronous output with the common stimulus for populations of simple neuron models. Within this frame, the synchronous activity is treated as the product of filtered versions of the spike trains of a subset of neurons. We compare our results for the simple cases of (1) a Poisson neuron with a rate modulation and (2) an LIF neuron with intrinsic white current noise and a current stimulus. For the Poisson neuron, formulas are particularly simple but show only a low-pass behavior of the coherence of synchronous activity. For the LIF model, in contrast, the coherence function of the synchronous activity shows a clear peak at high frequencies, comparable to recent experimental findings. We uncover the mechanism for this shift in the maximum of the coherence and discuss some biological implications of our findings.
Highlights
In many sensory modalities, time-varying stimuli are encoded by a population of many neurons (Gollisch and Meister 2008; Clemens et al 2011; Vonderschen and Chacron 2011)
J Comput Neurosci (2013) 34:285–301. Theoreticians have explored this simple setup mainly in population models of uncoupled neurons which are subject to intrinsic fluctuations and to common input stimulus (Knight 1972a; Stocks and Mannella 2001; Gerstner and Kistler 2002)
We have developed a framework for the analytical study of coding properties of synchrony in neural populations
Summary
Time-varying stimuli are encoded by a population of many neurons (Gollisch and Meister 2008; Clemens et al 2011; Vonderschen and Chacron 2011) Examples of such populations are found in common visual (Knight 1972b; Wandell 1995) and auditory systems (Hudspeth 2000) of many organisms, and in the more exotic electrosensory system of electric fish (Heiligenberg 1991; Krahe et al 2008). Theoreticians have explored this simple setup mainly in population models of uncoupled neurons which are subject to intrinsic fluctuations (e.g. channel noise or synaptic background noise) and to common input stimulus (arising from the overlap in the receptive fields) (Knight 1972a; Stocks and Mannella 2001; Gerstner and Kistler 2002). For a given stimulus amplitude a specific non-zero level of the intrinsic noise optimizes the mutual information between the common stimulus and the summed spiking activity of the population (Stocks and Mannella 2001)
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