Adaptive Resonance Theory and HTM

[rcrowder]

Adaptive resonance theory (ART, [1][2]) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information. It describes a number of neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction. Models that are capable of learning stable recognition categories.

https://en.wikipedia.org/wiki/Adaptive_resonance_theory

The primary intuition behind the ART model is that object identification and recognition generally occur as a result of the interaction of 'top-down' observer expectations with 'bottom-up' sensory information. The model postulates that 'top-down' expectations take the form of a memory template or prototype that is then compared with the actual features of an object as detected by the senses. This comparison gives rise to a measure of category belongingness. As long as this difference between sensation and expectation does not exceed a set threshold called the 'vigilance parameter', the sensed object will be considered a member of the expected class. The system thus offers a solution to the 'plasticity/stability' problem, i.e. the problem of acquiring new knowledge without disrupting existing knowledge.

In the context of NuPIC Audio, ART can be used (with sound localization) to separate auditory streams, dynamically manage categories, heading towards segmentation and identification of audio sources. An investigation can be conducted to the applicability of resonance theory (ART2A, [3][4][5]) with hierarchical temporal memory. A potential method of emulating current generation NN back propagation within HTM, and obtaining limited feedback and attention processing. A variety of changes will be required internal to NuPIC, such as column burst mechanism and Connections class expansion.

Ideas behind using ART with NuPIC formed via reading;

• Desimone and Duncan (1995) "Neural mechanisms of selective visual attention"
• Spratling (1999) "Pre-synaptic lateral inhibition provides a better architecture for self-organizing neural networks"
• Grossberg et al. (2004) "ARTSTREAM: A neural network model of auditory scene analysis and source segregation" [4]
• Ballard and Jehee (2011) "Dual roles for spike signaling in cortical neural populations"
• Ballard and Jehee (2012) "Dynamic coding of signed quantities in cortical feedback circuits"
• Spratling (2014) "A single functional model of drivers and modulators in cortex"

[1] G. A. Carpenter. Neural network models for pattern recognition and associative memory. Neural Networks 2, 4 (June 1989), 243-257. DOI=10.1016/0893-6080(89)90035-X http://dx.doi.org/10.1016/0893-6080(89)90035-X

[2] Carpenter, G. A. and Grossberg, S., Adaptive Resonance Theory. The Handbook of Brain Theory and Neural Networks, Second Edition, MIT Press, 2003.

[3] Carpenter, G.A. and Grossberg, S., ART2: Self-organization of stable category recognition codes for analog input patterns, Applied Optics, 26 (23): 4919-4930, 1987.

[4] Grossberg, Govindarajan, Wyse, Cohen (2004), ARTSTREAM: A neural network model of auditory scene analysis and source segregation, http://dx.doi.org/10.1016/j.neunet.2003.10.002

[5] https://github.com/rcrowder/AdaptiveResonanceTheory