Self-Organization in the Nervous System
This special issue reviews state-of-the-art approaches to the biophysical roots of cognition. These approaches appeal to the notion that cognitive capacities serve to optimize responses to changing external conditions. Crucially, this optimisation rests on the ability to predict changes in the envir...
I tiakina i:
| Ngā kaituhi matua: | , , |
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| Hōputu: | Online |
| Reo: | Ingarihi |
| I whakaputaina: |
Frontiers Media SA
2021
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| Ngā marau: | |
| Urunga tuihono: | 25641 |
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Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869525194823958528 |
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| author | Yan M. Yufik Biswa Sengupta Karl Friston |
| author_browse | Biswa Sengupta Karl Friston Yan M. Yufik |
| author_facet | Yan M. Yufik Biswa Sengupta Karl Friston |
| author_sort | Yan M. Yufik |
| collection | Directory of Open Access Books |
| description | This special issue reviews state-of-the-art approaches to the biophysical roots of cognition. These approaches appeal to the notion that cognitive capacities serve to optimize responses to changing external conditions. Crucially, this optimisation rests on the ability to predict changes in the environment, thus allowing organisms to respond pre-emptively to changes before their onset. The biophysical mechanisms that underwrite these cognitive capacities remain largely unknown; although a number of hypotheses has been advanced in systems neuroscience, biophysics and other disciplines. These hypotheses converge on the intersection of thermodynamic and information-theoretic formulations of self-organization in the brain. The latter perspective emerged when Shannon’s theory of message transmission in communication systems was used to characterise message passing between neurons. In its subsequent incarnations, the information theory approach has been integrated into computational neuroscience and the Bayesian brain framework. The thermodynamic formulation rests on a view of the brain as an aggregation of stochastic microprocessors (neurons), with subsequent appeal to the constructs of statistical mechanics and thermodynamics. In particular, the use of ensemble dynamics to elucidate the relationship between micro-scale parameters and those of the macro-scale aggregation (the brain). In general, the thermodynamic approach treats the brain as a dissipative system and seeks to represent the development and functioning of cognitive mechanisms as collective capacities that emerge in the course of self-organization. Its explicanda include energy efficiency; enabling progressively more complex cognitive operations such as long-term prediction and anticipatory planning. A cardinal example of the Bayesian brain approach is the free energy principle that explains self-organizing dynamics in the brain in terms of its predictive capabilities – and selective sampling of sensory inputs that optimise variational free energy as a proxy for Bayesian model evidence. An example of thermodynamically grounded proposals, in this issue, associates self-organization with phase transitions in neuronal state-spaces; resulting in the formation of bounded neuronal assemblies (neuronal packets). This special issue seeks a discourse between thermodynamic and informational formulations of the self-organising and self-evidencing brain. For example, could minimization of thermodynamic free energy during the formation of neuronal packets underlie minimization of variational free energy? |
| format | Online |
| id | doab-20.500.12854ir-59185 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media SA |
| publisherStr | Frontiers Media SA |
| record_format | ojs |
| spelling | doab-20.500.12854ir-591852024-04-05T17:30:58Z Self-Organization in the Nervous System Yan M. Yufik Biswa Sengupta Karl Friston RC321-571 Q1-390 consciousness understanding Markov blanket Hebbian assembly neuronal packet Bayesian brain thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences This special issue reviews state-of-the-art approaches to the biophysical roots of cognition. These approaches appeal to the notion that cognitive capacities serve to optimize responses to changing external conditions. Crucially, this optimisation rests on the ability to predict changes in the environment, thus allowing organisms to respond pre-emptively to changes before their onset. The biophysical mechanisms that underwrite these cognitive capacities remain largely unknown; although a number of hypotheses has been advanced in systems neuroscience, biophysics and other disciplines. These hypotheses converge on the intersection of thermodynamic and information-theoretic formulations of self-organization in the brain. The latter perspective emerged when Shannon’s theory of message transmission in communication systems was used to characterise message passing between neurons. In its subsequent incarnations, the information theory approach has been integrated into computational neuroscience and the Bayesian brain framework. The thermodynamic formulation rests on a view of the brain as an aggregation of stochastic microprocessors (neurons), with subsequent appeal to the constructs of statistical mechanics and thermodynamics. In particular, the use of ensemble dynamics to elucidate the relationship between micro-scale parameters and those of the macro-scale aggregation (the brain). In general, the thermodynamic approach treats the brain as a dissipative system and seeks to represent the development and functioning of cognitive mechanisms as collective capacities that emerge in the course of self-organization. Its explicanda include energy efficiency; enabling progressively more complex cognitive operations such as long-term prediction and anticipatory planning. A cardinal example of the Bayesian brain approach is the free energy principle that explains self-organizing dynamics in the brain in terms of its predictive capabilities – and selective sampling of sensory inputs that optimise variational free energy as a proxy for Bayesian model evidence. An example of thermodynamically grounded proposals, in this issue, associates self-organization with phase transitions in neuronal state-spaces; resulting in the formation of bounded neuronal assemblies (neuronal packets). This special issue seeks a discourse between thermodynamic and informational formulations of the self-organising and self-evidencing brain. For example, could minimization of thermodynamic free energy during the formation of neuronal packets underlie minimization of variational free energy? 2021-02-12T03:19:08Z 2021-02-12T03:19:08Z 2018-02-27 16:16:45 2017 book 25641 16648714 9782889453405 https://directory.doabooks.org/handle/20.500.12854/59185 eng Frontiers Research Topics image/jpeg Attribution 4.0 International https://www.frontiersin.org/books/Self-Organization_in_the_Nervous_System/1397#nogo https://www.frontiersin.org/research-topics/4050/self-organization-in-the-nervous-system Frontiers Media SA 10.3389/978-2-88945-340-5 10.3389/978-2-88945-340-5 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889453405 135 open access |
| spellingShingle | RC321-571 Q1-390 consciousness understanding Markov blanket Hebbian assembly neuronal packet Bayesian brain thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences Yan M. Yufik Biswa Sengupta Karl Friston Self-Organization in the Nervous System |
| title | Self-Organization in the Nervous System |
| title_full | Self-Organization in the Nervous System |
| title_fullStr | Self-Organization in the Nervous System |
| title_full_unstemmed | Self-Organization in the Nervous System |
| title_short | Self-Organization in the Nervous System |
| title_sort | self organization in the nervous system |
| topic | RC321-571 Q1-390 consciousness understanding Markov blanket Hebbian assembly neuronal packet Bayesian brain thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| topic_facet | RC321-571 Q1-390 consciousness understanding Markov blanket Hebbian assembly neuronal packet Bayesian brain thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| url | 25641 |
| work_keys_str_mv | AT yanmyufik selforganizationinthenervoussystem AT biswasengupta selforganizationinthenervoussystem AT karlfriston selforganizationinthenervoussystem |