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A Theory of Cerebral Neocortex

Explore David Marr's groundbreaking theories on the neocortex and cerebellum, delving into topics such as associative memory, redundancy reduction, and unsupervised learning. Discover Marr's insights on spatial recognition, processing uniformity, and the challenges of probabilistic classification. Unravel the complexities of information clustering, feature-based similarity, and the significance of mountains in neural processing. Assess Marr's approach to justifying biological utility, redundancy, and intrinsic properties. Engage with the debate on recoding mechanisms, initialization, and the implications for unsupervised learning. Join the discourse on Marr's legacy in cognitive science and the future directions of neural modeling.

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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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