{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T21:00:17Z","timestamp":1760821217748,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2019 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\u2019 fluctuation measure, I assume the standard deviation \u03c3, 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&lt;1, the quotient ITR\u03c3 has a maximum and can tend to zero depending on transition probabilities, while for 1&lt;s, the ITR\u03c3 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&lt;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.<\/jats:p>","DOI":"10.3390\/e23010092","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T19:55:56Z","timestamp":1610308556000},"page":"92","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Signal Fluctuations and the Information Transmission Rates in Binary Communication Channels"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9163-9931","authenticated-orcid":false,"given":"Agnieszka","family":"Pregowska","sequence":"first","affiliation":[{"name":"Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1126\/science.aax1512","article-title":"Parsing signal and noise in the brain","volume":"364","author":"Huk","year":"2019","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1126\/science.7770778","article-title":"Reliability of spike timing in neocortical neurons","volume":"268","author":"Mainen","year":"1995","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"van Hemmen, J.L., and Sejnowski, T. 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