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A Physicist’s Brain. J. C. Sprott Department of Physics University of Wisconsin - Madison Presented at the Chaos and Complex Systems Seminar In Madison, Wisconsin On October 18, 2005. Collaborators. David Albers , Max Planck Institute (Leipzig, Germany)
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A Physicist’s Brain J. C. Sprott Department of Physics University of Wisconsin - Madison Presented at the Chaos and Complex Systems Seminar In Madison, Wisconsin On October 18, 2005
Collaborators • David Albers, Max Planck Institute (Leipzig, Germany) • Matt Sieth, Univ Wisc - Undergrad
A Physicist’s Neuron N inputs tanh x x
Architecture N neurons
Artificial Neural Network (P-Brain) • Nonlinear, discrete-time, complex, dynamical system • “Universal” approximator (?) • aij chosen from a random Gaussian distribution with mean zero and standard deviation s • Two parameters: N and s • Arbitrary (large) N infinity • Initial conditions random in the range -1 to +1.
Attractor Dimension DKY = 0.46 N N
Summary of High-N Dynamics • Chaos is the rule • Maximum attractor dimension is of order N/2 • Quasiperiodic route is usual • Attractor is sensitive to parameter perturbations, but dynamics are not
P-Brain Artist • Train a neural network to produce art • Choose N= 6 • Find “good” regions of the 36-D parameter space • Randomly explore a neighborhood of that region
Automatic Preselection • Must be chaotic (positive Lyapunov exponent) • Not too “thin” (fractal dimension > 1) • Not too small or too large • Not too off-centered
Problem – Rugged Landscape Relative Error -5% 0 +5%
Information Content • Robin: 244 x 340 x 3 x 8 = 2 Mbits Compresses (gif) to 283 kbits Compresses (jpeg) to 118 kbits Compresses (png) to 1.8 Mbits • P-Brain: 36 x 5 = 180 bits • Cannot expect a good replica
Future Directions • More biological realism • More neurons • More realistic architecture • Training on real EEG data or task performance
http://sprott.physics.wisc.edu/ lectures/brain.ppt (this talk) sprott@physics.wisc.edu (contact me) References