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Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland
Entropy 2021, 23(1), 92; https://doi.org/10.3390/e23010092
Received: 1 December 2020 / Revised: 26 December 2020 / Accepted: 8 January 2021 / Published: 10 January 2021
(This article belongs to the Section Information Theory, Probability and Statistics)
In the nervous system, information is conveyed by sequence of action potentials, called spikes-trains. As MacKay and McCulloch suggested, spike-trains can be represented as bits sequences coming from Information Sources (IS). Previously, we studied relations between spikes’ Information Transmission Rates (ITR) and their correlations, and frequencies. Now, I concentrate on the problem of how spikes fluctuations affect ITR. The IS are typically modeled as stationary stochastic processes, which I consider here as two-state Markov processes. As a spike-trains’ fluctuation measure, I assume the standard deviation σ, which measures the average fluctuation of spikes around the average spike frequency. I found that the character of ITR and signal fluctuations relation strongly depends on the parameter s being a sum of transitions probabilities from a no spike state to spike state. The estimate of the Information Transmission Rate was found by expressions depending on the values of signal fluctuations and parameter s. It turned out that for smaller s<1, the quotient ITRσ has a maximum and can tend to zero depending on transition probabilities, while for 1<s, the ITRσ is separated from 0. Additionally, it was also shown that ITR quotient by variance behaves in a completely different way. Similar behavior was observed when classical Shannon entropy terms in the Markov entropy formula are replaced by their approximation with polynomials. My results suggest that in a noisier environment (1<s), to get appropriate reliability and efficiency of transmission, IS with higher tendency of transition from the no spike to spike state should be applied. Such selection of appropriate parameters plays an important role in designing learning mechanisms to obtain networks with higher performance. View Full-Text
Keywords: information source; information transmission rate; fluctuations; Shannon entropy; spike-trains; standard deviation information source; information transmission rate; fluctuations; Shannon entropy; spike-trains; standard deviation
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MDPI and ACS Style

Pregowska, A. Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels. Entropy 2021, 23, 92. https://doi.org/10.3390/e23010092

AMA Style

Pregowska A. Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels. Entropy. 2021; 23(1):92. https://doi.org/10.3390/e23010092

Chicago/Turabian Style

Pregowska, Agnieszka. 2021. "Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels" Entropy 23, no. 1: 92. https://doi.org/10.3390/e23010092

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