Applying Information Theory to Neuronal Networks: From Theory to Experiments
1
Computational Science, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
2
Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Entropy 2014, 16(11), 5721-5737; https://doi.org/10.3390/e16115721
Received: 3 June 2014 / Revised: 27 July 2014 / Accepted: 28 October 2014 / Published: 3 November 2014
(This article belongs to the Special Issue Entropy in Human Brain Networks)
Information-theory is being increasingly used to analyze complex, self-organizing processes on networks, predominantly in analytical and numerical studies. Perhaps one of the most paradigmatic complex systems is a network of neurons, in which cognition arises from the information storage, transfer, and processing among individual neurons. In this article we review experimental techniques suitable for validating information-theoretical predictions in simple neural networks, as well as generating new hypotheses. Specifically, we focus on techniques that may be used to measure both network (microcircuit) anatomy as well as neuronal activity simultaneously. This is needed to study the role of the network structure on the emergent collective dynamics, which is one of the reasons to study the characteristics of information processing. We discuss in detail two suitable techniques, namely calcium imaging and the application of multi-electrode arrays to simple neural networks in culture, and discuss their advantages and limitations in an accessible manner for non-experts. In particular, we show that each technique induces a qualitatively different type of error on the measured mutual information. The ultimate goal of this work is to bridge the gap between theorists and experimentalists in their shared goal of understanding the behavior of networks of neurons.
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Keywords:
information theory; neuronal networks; topology; dynamics; calcium imaging; multi-electrode arrays
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MDPI and ACS Style
Jung, T.I.; Vogiatzian, F.; Har-Shemesh, O.; Fitzsimons, C.P.; Quax, R. Applying Information Theory to Neuronal Networks: From Theory to Experiments. Entropy 2014, 16, 5721-5737. https://doi.org/10.3390/e16115721
AMA Style
Jung TI, Vogiatzian F, Har-Shemesh O, Fitzsimons CP, Quax R. Applying Information Theory to Neuronal Networks: From Theory to Experiments. Entropy. 2014; 16(11):5721-5737. https://doi.org/10.3390/e16115721
Chicago/Turabian StyleJung, Thijs I.; Vogiatzian, Filippos; Har-Shemesh, Omri; Fitzsimons, Carlos P.; Quax, Rick. 2014. "Applying Information Theory to Neuronal Networks: From Theory to Experiments" Entropy 16, no. 11: 5721-5737. https://doi.org/10.3390/e16115721
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