The structural aspects of neural dynamics and information flow
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 int...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IMR Press
2022-01-01
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| Series: | Frontiers in Bioscience-Landmark |
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| Online Access: | https://www.imrpress.com/journal/FBL/27/1/10.31083/j.fbl2701015 |
| _version_ | 1818355023420063744 |
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| author | JunHyuk Woo Kiri Choi Soon Ho Kim Kyungreem Han MooYoung Choi |
| author_facet | JunHyuk Woo Kiri Choi Soon Ho Kim Kyungreem Han MooYoung Choi |
| author_sort | JunHyuk Woo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T19:34:43Z |
| format | Article |
| id | doaj.art-deb334d17ae44c8eace1099930542a9a |
| institution | Directory Open Access Journal |
| issn | 2768-6701 |
| language | English |
| last_indexed | 2024-12-13T19:34:43Z |
| publishDate | 2022-01-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Frontiers in Bioscience-Landmark |
| spelling | doaj.art-deb334d17ae44c8eace1099930542a9a2022-12-21T23:33:50ZengIMR PressFrontiers in Bioscience-Landmark2768-67012022-01-0127101510.31083/j.fbl2701015S2768-6701(22)00353-7The structural aspects of neural dynamics and information flowJunHyuk Woo0Kiri Choi1Soon Ho Kim2Kyungreem Han3MooYoung Choi4Laboratory of Computational Neurophysics, Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology, 02792 Seoul, Republic of KoreaSchool of Computational Sciences, Korea Institute for Advanced Study, 02455 Seoul, Republic of KoreaLaboratory of Computational Neurophysics, Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology, 02792 Seoul, Republic of KoreaLaboratory of Computational Neurophysics, Convergence Research Center for Brain Science, Brain Science Institute, Korea Institute of Science and Technology, 02792 Seoul, Republic of KoreaDepartment of Physics and Astronomy and Center for Theoretical Physics, Seoul National University, 08826 Seoul, Republic of KoreaBackground: 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.https://www.imrpress.com/journal/FBL/27/1/10.31083/j.fbl2701015neuronal structureneural dynamicsneural informationinformation-theoretic analysisdirect/indirect hippocampal pathways |
| spellingShingle | JunHyuk Woo Kiri Choi Soon Ho Kim Kyungreem Han MooYoung Choi The structural aspects of neural dynamics and information flow Frontiers in Bioscience-Landmark neuronal structure neural dynamics neural information information-theoretic analysis direct/indirect hippocampal pathways |
| title | The structural aspects of neural dynamics and information flow |
| title_full | The structural aspects of neural dynamics and information flow |
| title_fullStr | The structural aspects of neural dynamics and information flow |
| title_full_unstemmed | The structural aspects of neural dynamics and information flow |
| title_short | The structural aspects of neural dynamics and information flow |
| title_sort | structural aspects of neural dynamics and information flow |
| topic | neuronal structure neural dynamics neural information information-theoretic analysis direct/indirect hippocampal pathways |
| url | https://www.imrpress.com/journal/FBL/27/1/10.31083/j.fbl2701015 |
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