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Integrated Information Coefficient Estimated from Neuronal Activity in Hippocampus-Amygdala Complex of Rats as a
Ivan A Nazhestkin1, Olga E Svarnik1,2
1Institute of Nano, Bio, Information, Cognitive and Socio-Humanitarian Sciences and Technologies (INBICST), Moscow Institute of Physics and Technology, 117303 Moscow, Russian Federation.
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.
