Chaos and Information Processing: A Heuristic Outline
This book is the first attempt to give a chaotic dynamics interpretation of processes having to do with category formation and pattern recognition by systems possessing simple hardware e.g. few degrees of freedom. It is multidisciplinary in its approach and would be useful to readers from various fields.
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algorithm amongst asymptotic asymptotic stability average basin of attraction bifurcation biological processor bits brain calculate chaos chaotic attractor chaotic dynamics characterised coexisting attractors complex compressibility consider control parameters cortex cortical curve deduced deterministic digits dimension discrete display dissipative distribution dynamical system entropy equations example existence external noise flow formation fractal dimensionality given hierarchical levels information dimensionality information processing initial conditions intermittent invariant measure involved iterations jumping language length limit cycle linear linguistic log2 logistic map Lorenz attractor Lyapunov exponent Markov chain Markovian memory microscopic motion multifractal neuronal Nicolis nonlinear one-dimensional orbits oscillator partition pattern points possess probability density random rank order recursive result scale scanning self-similar semantic separatrices sequence simulate space specific spectrum stable strange attractor subintervals subset symmetric syntactical thalamocortical pacemaker thereby tion trajectory transitional probabilities unit interval unstable variables words Zipf's law