Abstract
Life, thought of as adaptively organised complexity, depends upon information and inference, which is nearly always inductive, because the world, though lawful, is far from being wholly predictable. There are several influential theories of probabilistic inference in neural systems, but here I focus on the theory of Coherent Infomax, and its relation to the theory of free energy reduction. Coherent Infomax shows, in principle, how life can be preserved and improved by coordinating many concurrent inferences. It argues that neural systems combine local reliability with flexible, holistic, context-sensitivity. What this perspective contributes to our understanding of neuronal inference is briefly outlined by relating it to cognitive and neurophysiological studies of context-sensitivity and gain-control, psychotic disorganization, theories of the Bayesian brain, and predictive coding. Limitations of the theory and unresolved issues are noted, emphasizing those that may be of interest to philosophers, and including the possibility of major transitions in the evolution of inferential capabilities.
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References
Phillips, W.A., von der Malsburg, C., Singer, W.: Dynamic coordination in brain and mind. In: Von der Malsburg, C., Phillips, W.A., Singer, W. (eds.) Dynamic Coordination in the Brain: From Neurons to Mind. Strüngmann forum report, vol. 5, ch.1, pp. 1–24. MIT Press, Cambridge (2010)
Friston, K.J.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)
Phillips, W.A., Kay, J., Smyth, D.: The discovery of structure by multi-stream networks of local processors with contextual guidance. Network-Comp. Neural Systems 6, 225–246 (1995)
Kay, J., Floreano, D., Phillips, W.A.: Contextually guided unsupervised learning using local multivariate binary processors. Neural Networks 11, 117–140 (1998)
Kay, J., Phillips, W.A.: Coherent infomax as a computational goal for neural systems. B. Math. Biol. 73, 344–372 (2011), doi:10.1007/s11583-010-9564-x
Jaynes, E.T.: Probability Theory: The Logic of Science, Edited by G. Larry Bretthorst. Cambridge University Press, Cambridge (2003)
Engel, C., Singer, W.: Better than Conscious? Strüngmann forum report, vol. 1. MIT Press, Cambridge (2008)
Yuille, A., Kersten, D.: Vision as Bayesian inference: analysis by synthesis? Trends Cogn. Sci. 10(7), 301–308 (2006)
Knill, D.C., Pouget, A.: The Bayesian Brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712–719
Rao, R.P., Ballard, D.H.: Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2(1), 79–87 (1999/2004)
Barlow, H.B.: Inductive inference, coding, perception, and language. Perception 3, 123–134 (1974)
Barlow, H.B.: Possible principles underlying the transformations of sensory messages. In: Rosenblith, W. (ed.) Sensory Communication, pp. 217–234. MIT Press, Cambridge (1961)
Linsker, R.: Perceptual neural organization: some approaches based on network models and information theory. Annu. Rev. Neurosci. 13, 257–281 (1990)
Wolpert, D.M., Diedrichsen, J., Flanagan, J.R.: Principles of sensorimotor learning. Nat. Rev. Neurosci. (in press)
Friston, K., Kilner, J., Harrison, L.: A free energy principle for the brain. J. Physiol. Paris 100(1-3), 70–87 (2006)
Friston, K.J., Stephan, K.E.: Free-energy and the brain. Synthese 159, 417–458 (2007)
Phillips, W.A., Singer, W.: In search of common foundations for cortical computation. Behav. Brain Sci. 20, 657–722 (1997)
von der Malsburg, C., Phillips, W.A., Singer, W. (eds.): Dynamic coordination in the brain: from neurons to mind. Strüngmann forum report, vol. 5. MIT Press, Cambridge (2010)
Phillips, W.A., Silverstein, S.M.: Convergence of biological and psychological perspectives on cognitive coordination in schizophrenia. Behav. Brain Sci. 26, 65–138 (2003)
Phillips, W.A.: Self-organized complexity and Coherent Infomax from the viewpoint of Jaynes’s probability theory. Information 3(1), 1–15 (2012), doi:10.3390/info3010001
Clark, A.: Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. (in press)
Spratling, M.W.: Predictive-coding as a model of biased competition in visual attention. Vis. Res. 48, 1391–1408 (2008)
Wacongne, C., Changeaux, J.-P., Deheane, S.: A neuronal model of predictive coding accounting for the mismatch negativity. J. Neurosci. 32, 3665–3678 (2012)
Szmatháry, E., Maynard Smith, J.: The major evolutionary transitions. Nature 374, 227–232 (1995)
Ginter, F., Boberg, J., Jarvinen, J., Salakoski, T.: New techniques for disambiguation in natural language and their application to biological text. J. Mach. Learn. Res. 5, 605–621 (2004)
Phillips, W.A., Chapman, K.L.S., Berry, P.D.: Size perception is less context-sensitive in males. Perception 33, 79–86 (2004)
Doherty, M.J., Tsuji, H., Phillips, W.A.: The context-sensitivity of visual size perception varies across cultures. Perception 37, 1426–1433 (2008)
Uhlhaas, P.J., Phillips, W.A., Mitchell, G., Silverstein, S.M.: Perceptual grouping in disorganized schizophrenia. Psychiatry Research 145, 105–117 (2006)
Doherty, M.J., Campbell, N.M., Tsuji, H., Phillips, W.A.: The Ebbinghaus illusion deceives adults but not young children. Developmental Science, 1–8 (2009), doi:10.1111/j.1467-7687.2009.00931.x
Fiorillo, C.D.: On the need for a unified and Jaynesian definition of probability and information within neuroscience. Information 3, 175–203 (2012)
Phillips, W.A., Craven, B.J.: Interactions between coincident and orthogonal cues to texture boundaries. Percep and Psychophys 62, 1019–1038
Feldman, H., Friston, K.J.: Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience 4, 215 (2010)
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Phillips, W.A. (2013). The Coordination of Probabilistic Inference in Neural Systems. In: Dodig-Crnkovic, G., Giovagnoli, R. (eds) Computing Nature. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37225-4_3
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