Computability, complexity, and languages (2nd ed.): fundamentals of theoretical computer science
Computability, complexity, and languages (2nd ed.): fundamentals of theoretical computer science
Rich classes inferable from positive data
Information and Computation
Neural networks and analog computation: beyond the Turing limit
Neural networks and analog computation: beyond the Turing limit
Models of Computation: Exploring the Power of Computing
Models of Computation: Exploring the Power of Computing
Computation: finite and infinite machines
Computation: finite and infinite machines
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
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We present two simple arguments for the potential relevance of a neurobiological analogue of the finite-state architecture. The first assumes the classical cognitive framework, is well-known, and is based on the assumption that the brain is finite with respect to its memory organization. The second is formulated within a general dynamical systems framework and is based on the assumption that the brain sustains some level of noise and/or does not utilize infinite precision processing. We briefly review the classical cognitive framework based on Church-Turing computability and non-classical approaches based on analog processing in dynamical systems. We conclude that the dynamical neurobiological analogue of the finite-state architecture appears to be relevant, at least at an implementational level, for cognitive brain systems.