NDLI: Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

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Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels

Content Provider	MDPI
Author	Pregowska, Agnieszka
Copyright Year	2021
Abstract	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 ( $I S$ ). Previously, we studied relations between spikes’ Information Transmission Rates $(I T R)$ and their correlations, and frequencies. Now, I concentrate on the problem of how spikes fluctuations affect $I T R$ . The $I S$ 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 $I T R$ 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 $\frac{I T R}{σ}$ has a maximum and can tend to zero depending on transition probabilities, while for $1 < s$ , the $\frac{I T R}{σ}$ is separated from 0. Additionally, it was also shown that $I T R$ 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, $I S$ 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.
Starting Page	92
e-ISSN	10994300
DOI	10.3390/e23010092
Journal	Entropy
Issue Number	1
Volume Number	23
Language	English
Publisher	MDPI
Publisher Date	2021-01-10
Access Restriction	Open
Subject Keyword	Entropy Hardware and Architecturee Information Source Information Transmission Rate Fluctuations Shannon Entropy Spike-trains Standard Deviation
Content Type	Text
Resource Type	Article

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