Toward a Noncomputational Cognitive Neuroscience

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Abstract
The near universally accepted theory that the brain processes information persists in current neural network theory where there is "subsymbolic" computation (Smolensky, 1988) on distributed representations. This theory of brain information processing may suffice for simplifying models simulated in silicon but not for living neural nets where there is ongoing chemical tuning of the input/output transfer function at the nodes, connection weights, network parameters, and connectivity. Here the brain continually changes itself as it intersects with information from the outside. An alternative theory to information processing is developed in which the brain permits and supports "participation" of self and other as constraints on the dynamically evolving, self-organizing whole. The noncomputational process of "differing and deferring" in nonlinear dynamic neural systems is contrasted with Black's (1991) account of molecular information processing. State hyperspace for the noncomputational process of nonlinear dynamical systems, unlike classical systems, has a fractal dimension. The noncomputational model is supported by suggestive evidence for fractal properties of the brain.