towards an implementation of a theory of visual learning in the brain
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Towards an Implementation of a Theory of Visual Learning in the Brain. Shamit Patel CMSC 601 May 2, 2011. The Problem. To develop a working theory of learning in the human neocortex and implement it in software - PowerPoint PPT PresentationTRANSCRIPT
Towards an Implementation of a
Theory of Visual Learning in the Brain
Shamit PatelCMSC 601
May 2, 2011
The Problem• 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
Hypothesis• 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
Motivation• Medical image processing• Quality control• Surveillance• Ultimately, we would like to build
machines that operate on the same neurocomputational mechanisms as the human brain
From Von Neumann Architecture to Neural Architecture of the Brain
Image source: http://en.wikipedia.org/wiki/File:Von_Neumann_architecture.svg
Image source: http://bluebrain.epfl.ch/files/content/sites/bluebrain/files/bluebrain-neuron.jpg
Related Work• Numenta’s Hierarchical Temporal
Memory (HTM) model• Riesenhuber and Poggio’s HMAX
model• Fukushima’s Neocognitron model
The Human Neocortex
Image source: http://www.ncbi.nlm.nih.gov/books/NBK10870/bin/ch26f3.jpg
Hierarchical Temporal Memory
Image source: http://upload.wikimedia.org/wikipedia/en/8/87/HTM_Hierarchy_example.png
Hierarchical Temporal Memory• 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
HMAX
Image source: http://riesenhuberlab.neuro.georgetown.edu/hmaxSchemeCD.jpg
HMAX• 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]
Neocognitron
Image source: http://www.scholarpedia.org/wiki/images/9/9d/ScholarFig1.gif
Neocognitron• 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
Approach
1) Implementation of HTM system2) Integration of SensoReaction algorithm
into the HTM system3) Training the HTM system on temporal
image data4) Testing the HTM system on novel input
patterns5) Statistical analysis of results
Implementation of HTM system• 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
Integration of SensoReaction algorithm into HTM 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.
Training the HTM system• 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
Evaluation/Testing the HTM system• 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
Statistical Analysis of Results• 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
Feasibility of Approach• 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
Conclusion• 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
References• [1] Numenta, Inc. (2010,
December 10). Hierarchical Temporal Memory including HTM cortical learning algorithms (Version No. 0.2). Retrieved from http://www.numenta.com/htm-overview/education/HTM CorticalLearningAlgorithms.pdf
References• [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
References• [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.no-ip.info/other/books/neural/Neocognitron/1980 Neocognitron%20A%20Self-organizing %20Neural%20Network%20Model%20for%20a%20Mechanism%20of%20Pattern%20Recognition%20 Unaffected%20by%20Shift%20in%20Position.pdf
Questions?