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Correlated stochastic resonance

Banff International Research Station for Mathematical Innovation and Discovery

Even when repeatedly presented with the same stimulus, sensory neurons show high levels of inter-trial variability. Similarly high levels of variability are observed throughout the brain, leading us to wonder how variability affects the function of neural circuits. One the one hand, prior work on “stochastic resonance” (SR) has shown that random fluctuations can enhance information transmission by nonlinear circuit elements like neurons. Specifically, the thresholding inherent in spike generation means that much of the information contained within the membrane potential can fail to propagate downstream. Random membrane potential fluctuations “soften” spike thresholds, allowing more information to survive the spike-generation process. This phenomenon reflects a tradeoff between the positive effects of threshold-softening, and the negative effects of corrupting signals by noise. yyWhile membrane potential fluctuations are often correlated between neurons in vivo, the role of this collective behavior in SR is largely unknown. Concurrently to the SR studies, other work investigated the impact of correlations on signal encoding by noisy non-spiking populations. For these non-spiking models, coding performance is highest when the noise is absent altogether: the noise is always a hindrance to the population codes. Consequently, those studies cannot reveal conditions under which collective variability enhances information coding. Despite these limitations, the prior studies of non-spiking models show that — depending on the patterns of inter-neural correlation — correlations can mitigate corruption of signals by noise. Combining ideas about correlations, and about SR, my talk will show that correlated membrane potential fluctuations can soften neural spiking thresholds without substantially corrupting the underlying signals with noise, thereby significantly enhancing spiking neural information coding.

Mathematics; Biology and other natural sciences; Probability theory and stochastic processes; Mathematical biology

video/mp4

Author affiliation: University of Colorado School of Medicine

2016-06-07

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58248

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/