This research topic seeks to review the state-of-the-art in defining biophysical roots of cognition, under the notion that cognitive capacities serve to optimize organism’s responses to varying external conditions. Response optimization is predicated on the ability to predict changes in the environment thus allowing the organism to initiate preparations to such changes before their onset. Biophysical mechanisms responsible for these cognitive capacities remain largely unknown although a number of hypotheses has been advanced in systems neuroscience, biophysics and other disciplines. These hypotheses appear to converge at the intersection of thermodynamic and information-theoretic formulations of self organization in the brain. The latter formulation took shape when Shannon’s theory of message transmission in communication systems was adopted to characterizing interaction between neurons. In its subsequent forms, the informational approach became integrated into the computational theory of the mind and the Bayesian brain framework. The thermodynamic formulation stems from viewing the brain as an aggregation of stochastic microprocessors (neurons), which suggested applying the ideas of statistical mechanics and thermodynamics to elucidate relationship between micro-scale parameters and those of the macro-scale aggregation (the brain). The thermodynamic approach defines brain as a dissipative system and seeks to represent the development and functioning of cognitive mechanisms as collective capacities emerging in the course of self-organization.
An example of the Bayesian brain hypothesis is the free energy principle that explains self-organizing activities in the brain by virtue of its predictive capabilities associated with selective sampling of sensory inputs directed towards minimizing variational free energy in the samples. Another approach traces brain self-organization to the second law of thermodynamics expressed as a principle of free energy consumption in the least time. Still another proposal associates self-organization with phase transition in the neuronal substrate resulting in the formation of neuronal assemblies and suggests that minimization of thermodynamic free energy in the phase-separation surface underlies minimization of variational free energy.
This research topic encourages analysis of self-organization processes in the brain, seeking a productive reconciliation between thermodynamic and informational definitions of the underlying biophysical mechanisms. The objective is to elucidate relations between predictive capabilities of the brain and b) self-organization processes in the substrate. Analysis of such relations will help identifying biophysical roots of intelligence and inform the design of artifacts capable of autonomous performance. The discussion includes but is not limited to the following issues:
1. Synthesis of thermodynamic and informational theories of self-organization processes in the brain.
2. Experimental assessment of self-organization processes in the brain.
3. Brain self-organization and predictive capabilities.
This research topic seeks to review the state-of-the-art in defining biophysical roots of cognition, under the notion that cognitive capacities serve to optimize organism’s responses to varying external conditions. Response optimization is predicated on the ability to predict changes in the environment thus allowing the organism to initiate preparations to such changes before their onset. Biophysical mechanisms responsible for these cognitive capacities remain largely unknown although a number of hypotheses has been advanced in systems neuroscience, biophysics and other disciplines. These hypotheses appear to converge at the intersection of thermodynamic and information-theoretic formulations of self organization in the brain. The latter formulation took shape when Shannon’s theory of message transmission in communication systems was adopted to characterizing interaction between neurons. In its subsequent forms, the informational approach became integrated into the computational theory of the mind and the Bayesian brain framework. The thermodynamic formulation stems from viewing the brain as an aggregation of stochastic microprocessors (neurons), which suggested applying the ideas of statistical mechanics and thermodynamics to elucidate relationship between micro-scale parameters and those of the macro-scale aggregation (the brain). The thermodynamic approach defines brain as a dissipative system and seeks to represent the development and functioning of cognitive mechanisms as collective capacities emerging in the course of self-organization.
An example of the Bayesian brain hypothesis is the free energy principle that explains self-organizing activities in the brain by virtue of its predictive capabilities associated with selective sampling of sensory inputs directed towards minimizing variational free energy in the samples. Another approach traces brain self-organization to the second law of thermodynamics expressed as a principle of free energy consumption in the least time. Still another proposal associates self-organization with phase transition in the neuronal substrate resulting in the formation of neuronal assemblies and suggests that minimization of thermodynamic free energy in the phase-separation surface underlies minimization of variational free energy.
This research topic encourages analysis of self-organization processes in the brain, seeking a productive reconciliation between thermodynamic and informational definitions of the underlying biophysical mechanisms. The objective is to elucidate relations between predictive capabilities of the brain and b) self-organization processes in the substrate. Analysis of such relations will help identifying biophysical roots of intelligence and inform the design of artifacts capable of autonomous performance. The discussion includes but is not limited to the following issues:
1. Synthesis of thermodynamic and informational theories of self-organization processes in the brain.
2. Experimental assessment of self-organization processes in the brain.
3. Brain self-organization and predictive capabilities.
