Shifting Spike Times or Adding and Deleting Spikes—How Different Types of Noise Shape Signal Transmission in Neural Populations

• Published in: Journal of mathematical neuroscience Vol. 5; no. 1; pp. 1 - 35
• Main Authors: Voronenko, Sergej O, Stannat, Wilhelm, Lindner, Benjamin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2015; Springer Nature B.V; BioMed Central Ltd
• Subjects: Applications of Mathematics; Mathematical Modeling and Industrial Mathematics; Mathematics; Mathematics and Statistics; Neurosciences; Shifting of spikes; Common noise; Time-dependent input; Suprathreshold stochastic resonance; Population coding; Addition and deletion of spikes; Mutual information
• ISSN: 2190-8567; 2190-8567
• DOI: 10.1186/2190-8567-5-1

We study a population of spiking neurons which are subject to independent noise processes and a strong common time-dependent input. We show that the response of output spikes to independent noise shapes information transmission of such populations even when information transmission properties of single neurons are left unchanged. In particular, we consider two Poisson models in which independent noise either (i) adds and deletes spikes (AD model) or (ii) shifts spike times (STS model). We show that in both models suprathreshold stochastic resonance (SSR) can be observed, where the information transmitted by a neural population is increased with addition of independent noise. In the AD model, the presence of the SSR effect is robust and independent of the population size or the noise spectral statistics. In the STS model, the information transmission properties of the population are determined by the spectral statistics of the noise, leading to a strongly increased effect of SSR in some regimes, or an absence of SSR in others. Furthermore, we observe a high-pass filtering of information in the STS model that is absent in the AD model. We quantify information transmission by means of the lower bound on the mutual information rate and the spectral coherence function. To this end, we derive the signal–output cross-spectrum, the output power spectrum, and the cross-spectrum of two spike trains for both models analytically.