Shamit Patel
CMSC 601
May 2, 2011

• To develop a working theory of learning in the human neocortex and implement it in software
• Goal is for the learning algorithm to match or exceed human-level accuracy in visual pattern recognition and other hierarchical inference tasks

• My hypothesis is that the brain learns through a feedback loop of sensing and reacting. I call this theory SensoReaction.
• The brain essentially learns through experience
• Feedback is the crucial ingredient of intelligence because it allows the brain to refine its predictions into the correct answer

• Medical image processing
• Quality control
• Surveillance
• Ultimately, we would like to build machines that operate on the same neurocomputational mechanisms as the human brain

Image source: http://en.wikipedia.org/wiki/File:Von_Neumann_ar chitecture.svg
Image source: http://bluebrain.epfl.ch/files/content/sites/bluebrain
/files/bluebrain-neuron.jpg

• Numenta’s Hierarchical Temporal
Memory (HTM) model
• Riesenhuber and Poggio’s HMAX model
• Fukushima’s Neocognitron model

Image source: http://www.ncbi.nlm.nih.gov/books/NBK10870/bin/ch26f3.jpg

Image source: http://upload.wikimedia.org/wikipedia/en/8/87/HTM_Hierarchy_example.png

• Directly based on the structure and computational properties of the human neocortex [1]
• Four main tasks of HTM: learning, inference, prediction, and behavior [1]
• Strength: Efficiency due to hierarchical structure [1]
• Weakness: Needs lots of training data

Image source: http://riesenhuberlab.neuro.georgetown.edu/hmaxSchemeCD.jpg

• Models the behavior of the ventral visual stream [2]
• Fundamental operations: (1) Weighted linear sum for aggregating simple features into complex ones, (2) Highly nonlinear “MAX” operation that computes output based on most active input [2]
• Strengths: Efficiency and invariance to position and size of input pattern [2]
• Weakness: Poor generalization to objects of different classes [2]

Image source: http://www.scholarpedia.org/wiki/images/9/9d/ScholarFig1.gif

• Self-organized via unsupervised learning [3]
• S-cells are changeable and C-cells are invariant to position, shape, and size of input pattern [3]
• Strength: Unsupervised learning means we don’t need labeled data
• Weakness: Poor generalization to objects of different classes

1) Implementation of HTM system
2) Integration of SensoReaction algorithm into the HTM system
3) Training the HTM system on temporal image data
4) Testing the HTM system on novel input patterns
5) Statistical analysis of results

• I have already implemented a considerable part of the HTM system, including the overall structure of the network and most of the training functionality
• Remaining work consists of implementing inference and integrating SensoReaction into the system

• SensoReaction is a feedback propagation mechanism that allows predictions to be propagated down the hierarchy for correction
• Algorithm will be integrated into the HTM system by first introducing feedback connections between every pair of successive layers in the network. Then, predictions will be passed down the hierarchy via these feedback connections.

• Present hundreds of streams of temporal image data to the input layer
• Allow the system to build its internal representations
• Training will consist of: (1) memorizing patterns, (2) building the Markov graphs, and
(3) forming the temporal groups

• Present thousands of noisy input patterns to the HTM network
• Observe the classification accuracy of the HTM system
• SensoReaction algorithm comes into play here by making predictions, passing them down the hierarchy, correcting them, and passing them back up

• Classification accuracy of HTM system with SensoReaction will be compared with classification accuracy of standard HTM system
• Two-sample t-test will be used to compare the classification accuracies of the two systems

• SensoReaction is feasible because it is essentially based on how the neocortex processes feedback
• Feedback can only improve the classification accuracy because prior experience is taken into account

• Feedback is the critical piece of intelligence
• Brain learns through constant sensing and reacting
• Ultimate goal is to build machines that work on the same computational principles as the brain

• [1] Numenta, Inc. (2010,
December 10). Hierarchical
Temporal Memory including HTM cortical learning algorithms
(Version No. 0.2). Retrieved from http://www.numenta.com/htmoverview/education/HTM
CorticalLearningAlgorithms.pdf

• [2] Riesenhuber, M., & Poggio, T.
(1999, November). Hierarchical models of object recognition in cortex. Nature America, 2(11),
1019-1025. Retrieved from http://cbcl.mit.edu/publications/ ps/nn99.pdf

• [3] Fukushima, K. (1980). Neocognitron: a self-organizing neural network model for a mechanism of pat- tern recognition unaffected by shift in position. Biological
Cybernetics, 36, 193-202. Retrieved from http://lrn.noip.info/other/books/neural/Neocognitron/19
80 Neocognitron%20A%20Self-organizing
%20Neural%20Network%20Model%20for%2
0a%20Mechanism%20of%20Pattern%20Reco gnition%20
Unaffected%20by%20Shift%20in%20Position.
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