Predictive coding under the free-energy principle. Export

@article{citeulike:5488843, abstract = {This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.}, author = {Friston, Karl and Kiebel, Stefan}, citeulike-article-id = {5488843}, citeulike-linkout-0 = {http://dx.doi.org/10.1098/rstb.2008.0300}, citeulike-linkout-1 = {http://rstb.royalsocietypublishing.org/content/364/1521/1211.abstract}, citeulike-linkout-2 = {http://rstb.royalsocietypublishing.org/content/364/1521/1211.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19528002}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19528002}, day = {12}, doi = {10.1098/rstb.2008.0300}, issn = {1471-2970}, journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences}, keywords = {bayes, bayesian, dynamical\_systems, free\_energy, hierarchical\_models, perceptual\_inference, predictive\_coding, theoretical\_neuroscience}, month = {May}, number = {1521}, pages = {1211--1221}, posted-at = {2009-11-10 17:31:07}, priority = {2}, title = {Predictive coding under the free-energy principle.}, url = {http://dx.doi.org/10.1098/rstb.2008.0300}, volume = {364}, year = {2009} }

TY - JOUR ID - citeulike:5488843 L3 - citeulike-article-id:5488843 N2 - This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs. IS - 1521 JF - Philosophical transactions of the Royal Society of London. Series B, Biological sciences SN - 1471-2970 EP - 1221 TI - Predictive coding under the free-energy principle. VL - 364 SP - 1211 KW - bayes KW - bayesian KW - dynamical_systems KW - free_energy KW - hierarchical_models KW - perceptual_inference KW - predictive_coding KW - theoretical_neuroscience AU - Friston, Karl AU - Kiebel, Stefan PY - 2009/05/12/ UR - http://dx.doi.org/10.1098/rstb.2008.0300 ER -