Publication Date:
2020-05-14
Description:
The segregation of neural processing into distinct streams has been interpreted by someas evidence in favour of a modular view of brain function. This implies a set of specialised ‘modules’,each of which performs a specific kind of computation in isolation of other brain systems, beforesharing the result of this operation with other modules. In light of a modern understanding ofstochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanationpresents itself. Formulating the evolution of a non-equilibrium steady state system in terms of itsdensity dynamics reveals that such systems appear on average to perform a gradient ascent on theirsteady state density. If this steady state implies a sufficiently sparse conditional independencystructure, this endorses a mean-field dynamical formulation. This decomposes the density over allstates in a system into the product of marginal probabilities for those states. This factorisation lendsthe system a modular appearance, in the sense that we can interpret the dynamics of each factorindependently. However, the argument here is that it is factorisation, as opposed to modularisation,that gives rise to the functional anatomy of the brain or, indeed, any sentient system. In thefollowing, we briefly overview mean-field theory and its applications to stochastic dynamicalsystems. We then unpack the consequences of this factorisation through simple numericalsimulations and highlight the implications for neuronal message passing and the computationalarchitecture of sentience
Electronic ISSN:
1099-4300
Topics:
Chemistry and Pharmacology
,
Physics
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