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The Freeman Model as an Associative Memory: Application to Static Pattern Recognition. Mark D. Skowronski, John G. Harris, and Jose C. Principe Computational NeuroEngineering Lab Electrical and Computer Engineering University of Florida, Gainesville, FL April 25, 2004. Introduction. - PowerPoint PPT Presentation
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The Freeman Model as an Associative Memory:
Application to Static Pattern Recognition
Mark D. Skowronski, John G. Harris, and Jose C. PrincipeComputational NeuroEngineering LabElectrical and Computer EngineeringUniversity of Florida, Gainesville, FL
April 25, 2004
2
Introduction
This work funded by the Office of Naval Research grant N00014-1-1-0405
Freeman’s Reduced KII Network
• Freeman model fundamentals• Model hierarchy• Associative memory• Experiments• Conclusions
3
Freeman Model
Hierarchical nonlinear dynamic model of cortical signal processing from rabbit olfactory neo-cortex.
Reduced KII (RKII) cell (stable oscillator)
Q(m)Kabggb)(agab
1
IQ(g)Kabmmb)(amab
1
mg
gm
K0 cell, H(s) 2nd order low pass filter
Q(x)
4
RKII Network
High-dimensional, scalable network of stable oscillators.Fully connected M-cell and G-cell weight matrices (zero diagonal).
Capable of several dynamic behaviors:• Stable attractors (limit cycle, fixed point)• Chaos• Spatio-temporal patterns• Synchronization
Generalization Associative Memory
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Oscillator Network
Synchronization Through Stimulation (STS)
Two regimes of operation as an associative memory of binary patterns:
Energy Readout
Network weights for each regime set by outer product rule variation and by hand.
M. D. Skowronski and J. G. Harris, Phys. Rev. E, 2004 (in preparation)
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Associative MemoryInput Output Input Output
Partial:14/22 34/12
Noisy:13/25 31/21
Spurious:22/26 24/22
Full:0/30 30/0
Hamming:“zero”/“one”
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Two-Class Case
ASR with RKII Network
• \IY\ from “she”• \AA\ from “dark”• 10 HFCC-E coeffs.
converted to binary• Energy readout RKII
associative memory• No overlap between
learned centroids
Classifier \IY\ \AA\ % Correct
2nd order, \IY\ 2705 0 99.9
continuous \AA\ 8 4340
2nd order, \IY\ 2701 4 98.4
binary \AA\ 110 4238
1st order, \IY\ 2658 47 93.7
binary \AA\ 394 3954
RKII, \IY\ 2593 6 87.3
exact \AA\ 202 3564
RKII, \IY\ 2666 39 92.7
Hamming \AA\ 479 3869
RKII associative memory limited to 1st order, binary performance due to preprocessing restrictions.
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ASR with RKII Network
Three-Class Case• \IY\ from “she”• \AA\ from “dark”• \AE\ from “ask”• 18 HFCC-E coeffs.
converted to binary• Energy-based RKII
associative memory• Variable overlap between
learned centroids
Overlap controlled by binary feature conversionMore overlap more spurious outputs
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• Demonstrated static pattern classification using RKII associative memory,
• Oscillator network allows for synchronization,• Associative memory limited by binary feature
conversion and 1st order statistics,• Same issues as Hopfield associative memory:
spurious outputs, capacity, overlap,• Training by variation of outer product rule and hand
tuning.
Conclusions