A Bayesian and Emergent View of the Brain

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Very simple psychophysiological visual tests suggest that the brain, instead of processing visual information in a passive way as was classically thought, in fact actively evaluates probabilities of the causes of visual data and continuously proposes to the mind the ones that are more likely to account for sensory inputs. In the past few years, Karl Friston, a researcher from University College of London, and his group have proposed a mechanism by which the brain successfully performs with great precision the inversion of probability densities necessary for this Bayesian computation. This mechanism would account for several anatomic structures of the cortex, explaining in particular the abundance of backwards interneuronal connections. The proposed picture of brain functioning is that of a dynamical process, far from the static image of a photographic plate. The result is an emergence, for the final picture of the world is a coherent vision where the more likely causes are proposed in a coherent manner. Although the theory accounts for the automatic, infraconscious side of the processing of information in the brain, it is in good accord with Roger Sperry’s theory of consciousness as a theory of strong emergence. It is too soon to evaluate the solidity of the law of “minimization of free energy” proposed by Friston not only as ruling the automatisms of the brain but as a general law of biology. This law is similar (although in contradistinction) to the second law of thermodynamics of increase of entropy (insofar as it explains the tendency of living beings for self-organization), and it is already looked at by some neuroscientists as a big step forward in deciphering the mysteries of the brain.

A Bayesian and Emergent View of the Brain

in KronoScope



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    A domino of buttons in relief . . .
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    . . . and its complementary domino (or, perhaps, the same?).
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    Each of the eight diagrams shows the strength of the response of the same population of neurons of the primary motor area M1 when a monkey performs a movement (with a two-dimensional joystick) in the direction shown by the corresponding arrow in the center diagram. Each neuronal discharge is represented by a vector pointing in the direction of the preferred direction of this neuron, the length of which is proportional to the discharge rate of the neuron. The vectorial sum of all these individual discharges is displayed as a dotted vector, which is shown to point very near the actual direction of the monkey’s movement. After Georgopoulos et al. (1983), with permission.
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    Human intentionality in a Bayesian perspective. After the selection of the most probable interpretation of the sensory data through a Bayesian protocol comparing the sensory data with a memorized repertoire of similar inputs, this interpretation is transmitted to the decision centers where it is compared to the repertoire of the possible actions (a step where again the memory plays a crucial role). The final decision depends on the expected gain for each possible decision. After Ernst and Bültoff (2004), with permission, and Stanislas Dehaenne (2012), Cours du Collège de France.
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