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A Theory of Cerebral Neocortex

A Theory of Cerebral Neocortex. Marr 1970 Proc Roy Soc. David Marr 1945-1980. Three Key Theories. cerebellum (1969) [2341 citations] feedforward associative memory anatomy: granule cells=codons climbing fibres as teaching signals

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A Theory of Cerebral Neocortex

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  1. A Theory of Cerebral Neocortex Marr 1970 Proc Roy Soc

  2. David Marr 1945-1980

  3. Three Key Theories • cerebellum (1969) [2341 citations] • feedforward associative memory • anatomy: • granule cells=codons • climbing fibres as teaching signals • substantial attention to subtractive/divisive inhibition; threshold setting • neocortex (1970) [384 citations] • archicortex (1971) [1594 citations] • recurrent autoassociative memory • offline consolidation process for neocortex • MIT: famously abandoned modelling style -> Vision

  4. Neocortex • general theory • canonical microcircuit • unsupervised learning • what’s the statistical structure? • how can we be sure that this matters? • how do we initialize learning? • umbrella of information theory • realization in terms of clustering • mountains • numerical taxonomy (Jardine & Sibson) • what counts as evidence (codons) • mapping onto the anatomy

  5. Recycling • output cells (P-cells) no fixed meaning • ‘some aspect of the structure of information’ • codons (granule cells) could support probabilistic classification

  6. Redundancy (1) • redundancy reduction is fine near periphery • ‘diversity of specialized coding tricks’ • centrally: (iv)

  7. Redundancy (2) • information storage via probabilistic recoding • no general probabilistic model • but feature-based similarity is common • ‘some objects share features’ • implies mountains

  8. Five Questions • determining the appropriate features (codons) • finding the mountains (spatial recognition) • diagnosing new input in terms of its associated mountain • interpreting partial input in terms of its associated mountain • justifying the mountain in the first place

  9. Justification • internal biological utility • redundancy • uniformity of processing • external biological utility • extrinsic vs intrinsic properties

  10. Mixture of Mountains • spatial recognition, diagnosis, interpretation via EM; ML density estimation • but: • what is if has never been seen before? • similarity is not the same as frequency:

  11. Marr’s Solution • a bit unconvincing

  12. Discussion • gets to the heart of the problem of unsupervised learning • oddly not self-consistent • ‘special’ recoding mechanisms from spatial/temporal contiguity are different? • oddly not ML DE • before EM had been invented • much more to be said: notably recoding; initializing; mountain climbing…

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