Sinbad: A Neocortical Mechanism for Discovering Environmental Variables and Regularities Hidden in Sensory Input
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
We propose that a top priority of the cerebral cortex must be the discovery and explicit representation of the environmental variables that contribute as major factors to environmental regularities. Any neural representation in which such variables are represented only implicitly (thus requiring extra computing to use them) will make the regularities more complex and therefore more difficult, if not impossible, to learn. The task of discovering such important environmental variables is not an easy one, since their existence is only indirectly suggested by the sensory input patterns the cortex receives – these variables are “hidden.” We present a candidate computational strategy for (1) discovering regularity-simplifying environmental variables, (2) learning the regularities, and (3) using regularities in perceptual and decision-making tasks. The SINBAD computational model discovers useful environmental variables through a search for different, but nevertheless highly correlated functions of any kind over non-overlapping subsets of the known variables, this being indicative of some important environmental variable that is responsible for the correlation. We suggest that such a search is performed in the neocortex by the dendritic trees of individual pyramidal cells. According to the SINBAD model, the basic function of each pyramidal cell is (1) to discover and represent one of the regularity-simplifying environmental variables, and (2) to learn to infer the state of its variable from the states of other variables, represented by other pyramidal cells. A network of such cells – each cell just attending to representation of its variable – can function as a sophisticated and useful inferential model of the outside world.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Dan Ryder & Oleg Favorov (2001). The New Associationism: A Neural Explanation of the Predictive Powers of the Cerebral Cortex. [REVIEW] Brain and Mind 2 (2):161-194.
Hans-Rolf Gregorius (2011). The Analysis of Association Between Traits When Differences Between Trait States Matter. Acta Biotheoretica 59 (3):213-229.
Peter Gärdenfors (2004). Emulators as Sources of Hidden Cognitive Variables. Behavioral and Brain Sciences 27 (3):403-403.
Abner Shimony (1984). Contextual Hidden Variables Theories and Bell's Inequalities. British Journal for the Philosophy of Science 35 (1):25-45.
Peter Spirtes, Christopher Meek & Thomas Richardson, Causal Inference in the Presence of Latent Variables and Selection Bias.
Jack Vromen (2010). Where Economics and Neuroscience Might Meet. Journal of Economic Methodology 17 (2):171-183.
Frank Arntzenius (1994). Relativistic Hidden Variable Theories? Erkenntnis 41 (2):207 - 231.
Natasha Alechina (2000). Functional Dependencies Between Variables. Studia Logica 66 (2):273-283.
John A. Barnden & Kankanahalli Srinivas (1996). Quantification Without Variables in Connectionism. Minds and Machines 6 (2):173-201.
Adonai S. Sant'anna (2000). Elementary Particles, Hidden Variables, and Hidden Predicates. Synthese 125 (1-2):233 - 245.
Jonathan Cohen & Samuel C. Rickless (2007). Binding Arguments and Hidden Variables. Analysis 67 (1):65–71.
Samson Abramsky (2013). Relational Hidden Variables and Non-Locality. Studia Logica 101 (2):411-452.
Added to index2010-12-22
Total downloads12 ( #182,548 of 1,696,592 )
Recent downloads (6 months)5 ( #113,990 of 1,696,592 )
How can I increase my downloads?