IMR Press / JIN / Volume 21 / Issue 5 / DOI: 10.31083/j.jin2105128
Open Access Short Communication
Integrated Information Coefficient Estimated from Neuronal Activity in Hippocampus-Amygdala Complex of Rats as a Measure of Learning Success
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1 Institute of Nano, Bio, Information, Cognitive and Socio-Humanitarian Sciences and Technologies (INBICST), Moscow Institute of Physics and Technology, 117303 Moscow, Russian Federation
2 Institute of Psychology of Russian Academy of Sciences, 129366 Moscow, Russian Federation
*Correspondence:; (Ivan A. Nazhestkin)
These authors contributed equally.
Academic Editor: Imran Khan Niazi
J. Integr. Neurosci. 2022, 21(5), 128;
Submitted: 14 April 2022 | Revised: 28 April 2022 | Accepted: 5 May 2022 | Published: 21 July 2022
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Background: The goal of the brain is to provide right on time a suitable earlier-acquired model for the future behavior. How a complex structure of neuronal activity underlying a suitable model is selected or fixated is not well understood. Here we propose the integrated information Φ as a possible metric for such complexity of neuronal groups. It quantifies the degree of information integration between different parts of the brain and is lowered when there is a lack of connectivity between different subsets in a system. Methods: We calculated integrated information coefficient (Φ) for activity of hippocampal and amygdala neurons in rats during acquisition of two tasks: spatial task followed by spatial aversive task. An Autoregressive Φ algorithm was used for time-series spike data. Results: We showed that integrated information coefficient Φ is positively correlated with a metric of learning success (a relative number of rewards). Φ for hippocampal neurons was positively correlated with Φ for amygdalar neurons during the learning requiring the cooperative work of hippocampus and amygdala. Conclusions: This result suggests that integrated information coefficient Φ may be used as a prediction tool for the suitable level of complexity of neuronal activity and the future success in learning and adaptation and a tool for estimation of interactions between different brain regions during learning.

integrated information theory
20-34-90023/Russian Foundation for Basic Research
Fig. 1.
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