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The Freeman Model as an Associative Memory: Application to Static Pattern Recognition

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.

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The Freeman Model as an Associative Memory: Application to Static Pattern Recognition

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  1. 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

  2. Introduction • Freeman model fundamentals • Model hierarchy • Associative memory • Experiments • Conclusions Freeman’s Reduced KII Network This work funded by the Office of Naval Research grant N00014-1-1-0405

  3. K0 cell, H(s) 2nd order low pass filter Q(x) Reduced KII (RKII) cell (stable oscillator) Freeman Model Hierarchical nonlinear dynamic model of cortical signal processing from rabbit olfactory neo-cortex.

  4. Generalization Associative Memory 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

  5. Energy Readout Synchronization Through Stimulation (STS) Network weights for each regime set by outer product rule variation and by hand. Oscillator Network Two regimes of operation as an associative memory of binary patterns: M. D. Skowronski and J. G. Harris, Phys. Rev. E, 2004 (in preparation)

  6. Full: Hamming: “zero”/“one” 0/30 30/0 Partial: 14/22 34/12 Noisy: 13/25 31/21 Spurious: 22/26 24/22 Associative Memory Input Output Input Output

  7. RKII associative memory limited to 1st order, binary performance due to preprocessing restrictions. ASR with RKII Network Two-Class Case • \IY\ from “she” • \AA\ from “dark” • 10 HFCC-E coeffs. converted to binary • Energy readout RKII associative memory • No overlap between learned centroids

  8. Overlap controlled by binary feature conversion More overlap more spurious outputs 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

  9. Conclusions • 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.

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