Continuous-time symmetric Hopfield nets are computationally universal.

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Asymmetric continuous-time neural networks without local traps for solving constraint satisfaction problems.General-purpose computation with neural networks: a survey of complexity theoretic results.

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Continuous-time symmetric Hopfield nets are computationally universal.

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Continuous-time symmetric Hopfield nets are computationally universal.

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Continuous-time symmetric Hopfield nets are computationally universal.

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Continuous-time symmetric Hopfield nets are computationally universal.

@en

Continuous-time symmetric Hopfield nets are computationally universal.

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Continuous-time symmetric Hopfield nets are computationally universal.

@en

Continuous-time symmetric Hopfield nets are computationally universal.

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P1476

Continuous-time symmetric Hopfield nets are computationally universal.

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P2093

Jirí Síma

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Neural networks and physical systems with emergent collective computational abilities

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Neurons with graded response have collective computational properties like those of two-state neurons.

Analog neural nets with gaussian or other common noise distribution cannot recognize arbitrary regular languages.

The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.

On the computational complexity of binary and analog symmetric hopfield nets

Analog computation via neural networks

Computational power of neural networks: a characterization in terms of Kolmogorov complexity

A Theory of Complexity for Continuous Time Systems

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scholarly article

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10.1162/089976603321192130

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Pekka Orponen

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2003-03-01T00:00:00Z

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10871193

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12620163

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Continuous-time symmetric Hopfield nets are computationally universal.

about

P2860

P2860

Continuous-time symmetric Hopfield nets are computationally universal.

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P1433

P1476

P2093

P2860

P304

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P356

P433

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P577

P5875

P698

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P1433

P1476

P2093

P2860

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P31

P356

P433

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P50

P577

P5875

P698