[Comp-neuro] New Ph.D. Thesis: Model for Learning & Recognition in
dil at stanford.edu
Mon Aug 25 08:26:42 CEST 2008
I am pleased to announce the availability of my Ph.D. dissertation,
completed this June at the Department of Electrical Engineering at
Stanford University. The thesis is titled "How the brain might work: A
Hierarchical and Temporal Model for Learning and Recognition"
This thesis makes the following contributions:
1) Algorithms and networks, collectively called Hierarchical Temporal
Memory (HTM), used to learn hierarchical models of data. The HTM
algorithms, when applied to a visual pattern recognition problem,
exhibit invariant recognition, robustness to noise and generalization.
Inference in the hierarchy is performed using Bayesian belief
propagation equations adapted to HTMs.
2) An analysis of learning in hierarchical systems: Computational
learning theory does not offer any justification for the efficiency of
hierarchical learning. This dissertation proposes a generative model
for hierarchical spatio-temporal data and uses this model to analyze
the efficiency of hierarchical learning.
3) A mathematical model for cortical microcircuits: The microcircuit
model is derived by combining known anatomical constraints with the
computational specifications for Bayesian belief propagation in HTMs.
The proposed model has a laminar and columnar organization that
matches many known anatomical features. The circuit model is then used
in the modeling of two well known physiological phenomena including
the illusory contour effect.
The thesis can be downloaded from http://www.numenta.com/for-developers/education/DileepThesis.pdf
Parts of this thesis are incorporated into the NuPIC software
developed by Numenta (available for download from www.numenta.com).
Thanks and Best Regards
-- Dileep George
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