Analogy or identity: brain and machine at the Macy conferences on cybernetics
ACM SIGBIO Newsletter - Special edition on biologically motivated computing
Analog computation via neural networks
Theoretical Computer Science
On the computational power of neural nets
Journal of Computer and System Sciences
Analog computation with dynamical systems
PhysComp96 Proceedings of the fourth workshop on Physics and computation
Stochastic analog networks and computational complexity
Journal of Complexity
Neural networks and analog computation: beyond the Turing limit
Neural networks and analog computation: beyond the Turing limit
The computer and the brain
Computational power of neural networks: a characterization in terms of Kolmogorov complexity
IEEE Transactions on Information Theory
Hypercomputation: philosophical issues
Theoretical Computer Science - Super-recursive algorithms and hypercomputation
Philosophy of Mind Is (in Part) Philosophy of Computer Science
Minds and Machines
A hierarchical classification of first-order recurrent neural networks
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
The expressive power of analog recurrent neural networks on infinite input streams
Theoretical Computer Science
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``Neural computing'' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation'', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing that gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machine.