Transcript (pptx)
Pulsed Neural Networks Neil E. Cotter ECE Department University of Utah Overview Challenging Problems Artificial Neural Networks Learning Dynamic Networks Pulsed Networks Applications Challenging Problems Challenging Problems Image recognition in varied settings Speech recognition in noise Robotic control and navigation Artificial Neural Networks Biological Neuron Biological Neuron t0 w0 t t1 S T w1 t t2 w2 t t tn Perceptron w0 x0 w1 x1 S w2 x2 S y y Perceptron Response y(x1, x2) x2 y=1 y=0 x1 Linear Separability 0 0 0 1 0 0 1 1 1 0 11 1 1 1 0 0 0 0 0 1 x2 1 Linear Separability 0 0 0 1 0 0 1 1 1 0 11 1 1 1 0 0 0 0 0 1 x2 1 Logic Gates x1 1 0 (0, 1) (1, 1) y=1 y=0 0 0 (0, 0) (1, 0) x2 Parallel Processing Sigmoid Neuron w0 x0 w1 x1 S w2 x2 S y y Sigmoid Neuron Response y(x1, x2) x2 x1 Neural Network u1 uL Neuron Neuron wt S Neuron Neuron wt S Neuron Neuron wt S Neuron Neuron wt S y1 yM Neural Network u1 uL Line AND gate wt S Line AND gate wt S Line AND gate wt S Line AND gate wt S y1 yM Universal Approximation y x2 x1 Universal Approximation y x2 01 0 1 1 0 x1 Universal Approximation y x2 x1 Function Approximations Learning Gradient Descent E(w1, w2) w2 w1 Local Minima E(w1, w2) w2 w1 Backward-Error Propagation E(w1, w2) w2 w1 Dynamic Networks Dynamic Networks Neural Network ∫ dt ∫ dt x1 xN u1 y1 uL yM Learning through Time State 0 or 1 1 v1 x noise v16 Adaptive Critic p reward = ±1 filter adaptation w1 x16 x xq q noise Associative Search q y w16 ±F x Learning through Time State 0 or 1 x1 v1 noise v16 x x qq x16 w16 reward = ±1 l decay w1 Adaptive Critic p delay a b noise ^r Associative Search q y ± F Pulsed Networks Pulsed Neuron t0 t w0 t t1 t S T w1 t t t2 w2 t t t tn Pattern Processing T t t0 t1 t2 Response Regions 0 x2 Perceptron Pulsed Neuron x3 x3 0 0 1 1 0 0 x1 x2 0 x1 Pattern Windows t0 t1 t2 t Pattern Windows t0 t1 t2 t Dynamic Pulsed Networks Pulsed Neural Network x1 xN u1 y1 uL yM Applications Speech Recognition t w Sound Window Spectrum Auditory System Basilar Membrane Sound t t Research Opportunities Mathematically describe pattern processing Devise pattern-learning algorithms Build circuits Title