IMR Press / FBL / Volume 27 / Issue 1 / DOI: 10.31083/j.fbl2701015
Open Access Original Research
The structural aspects of neural dynamics and information flow
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1 Laboratory of Computational Neurophysics, Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology, 02792 Seoul, Republic of Korea
2 Department of Physics and Astronomy and Center for Theoretical Physics, Seoul National University, 08826 Seoul, Republic of Korea
3 School of Computational Sciences, Korea Institute for Advanced Study, 02455 Seoul, Republic of Korea
*Correspondence: (Kyungreem Han); (MooYoung Choi)
Academic Editor: Igor Lavrov
Front. Biosci. (Landmark Ed) 2022, 27(1), 15;
Submitted: 11 October 2021 | Revised: 6 December 2021 | Accepted: 27 December 2021 | Published: 12 January 2022
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Background: Neurons have specialized structures that facilitate information transfer using electrical and chemical signals. Within the perspective of neural computation, the neuronal structure is an important prerequisite for the versatile computational capabilities of neurons resulting from the integration of diverse synaptic input patterns, complex interactions among the passive and active dendritic local currents, and the interplay between dendrite and soma to generate action potential output. For this, characterization of the relationship between the structure and neuronal spike dynamics could provide essential information about the cellular-level mechanism supporting neural computations. Results: This work describes simulations and an information-theoretic analysis to investigate how specific neuronal structure affects neural dynamics and information processing. Correlation analysis on the Allen Cell Types Database reveals biologically relevant structural features that determine neural dynamics—eight highly correlated structural features are selected as the primary set for characterizing neuronal structures. These features are used to characterize biophysically realistic multi-compartment mathematical models for primary neurons in the direct and indirect hippocampal pathways consisting of the pyramidal cells of Cornu Ammonis 1 (CA1) and CA3 and the granule cell in the dentate gyrus (DG). Simulations reveal that the dynamics of these neurons vary depending on their specialized structures and are highly sensitive to structural modifications. Information-theoretic analysis confirms that structural factors are critical for versatile neural information processing at a single-cell and a neural circuit level; not only basic AND/OR but also linearly non-separable XOR functions can be explained within the information-theoretic framework. Conclusions: Providing quantitative information on the relationship between the structure and the dynamics/information flow of neurons, this work would help us understand the design and coding principles of biological neurons and may be beneficial for designing biologically plausible neuron models for artificial intelligence (AI) systems.

Neuronal structure
Neural dynamics
Neural information
Information-theoretic analysis
Direct/indirect hippocampal pathways
Fig. 1.
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