Separating the polynomial-time hierarchy by oracles
Proc. 26th annual symposium on Foundations of computer science
Dynamics of positive automata networks
Theoretical Computer Science
On the construction of parallel computers from various bases of Boolean functions
Theoretical Computer Science
The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
The complexity of Boolean functions
The complexity of Boolean functions
Sequential simulation of parallel iterations and applications
Theoretical Computer Science
Lyapunov functions associated to automata networks
Centre National de Recherche Scientifique on Automata networks in computer science: theory and applications
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Structural complexity 1
Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
Neural and automata networks: dynamical behavior and applications
Neural and automata networks: dynamical behavior and applications
Simple local search problems that are hard to solve
SIAM Journal on Computing
Efficient simulation of finite automata by neural nets
Journal of the ACM (JACM)
Some notes on threshold circuits, and multiplication in depth 4
Information Processing Letters
On the computational power of sigmoid versus boolean threshold circuits (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Depth-Size Tradeoffs for Neural Computation
IEEE Transactions on Computers - Special issue on artificial neural networks
On threshold circuits and polynomial computation
SIAM Journal on Computing
Reduced order LQG controllers for linear time varying plants
Systems & Control Letters
Threshold circuits of bounded depth
Journal of Computer and System Sciences
Introduction to the theory of complexity
Introduction to the theory of complexity
Explicit Constructions of Depth-2 Majority Circuits for Comparison and Addition
SIAM Journal on Discrete Mathematics
On Optimal Depth Threshold Circuits for Multiplication andRelated Problems
SIAM Journal on Discrete Mathematics
Circuit complexity and neural networks
Circuit complexity and neural networks
On the Size of Weights for Threshold Gates
SIAM Journal on Discrete Mathematics
Analog computation via neural networks
Theoretical Computer Science
On the node complexity of neural networks
Neural Networks
Discrete neural computation: a theoretical foundation
Discrete neural computation: a theoretical foundation
On the computational power of neural nets
Journal of Computer and System Sciences
Toward massively parallel design of multipliers
Journal of Parallel and Distributed Computing
On the geometric separability of Boolean functions
Discrete Applied Mathematics
The dynamic universality of sigmoidal neural networks
Information and Computation
A family of universal recurrent networks
Theoretical Computer Science - Special issue on universal machines and computations
Computing with truly asynchronous threshold logic networks
Theoretical Computer Science
Fast sigmoidal networks via spiking neurons
Neural Computation
Bounds for the Computational Power and Learning Complexity of Analog Neural Nets
SIAM Journal on Computing
Self-organizing maps
Approximability of the ground state problem for certain Ising spin glasses
Journal of Complexity
Journal of the ACM (JACM)
Simulating Threshold Circuits by Majority Circuits
SIAM Journal on Computing
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
The structure of logarithmic advice complexity classes
Theoretical Computer Science - Special issue In Memoriam of Ronald V. Book
Information and Computation
On the effect of analog noise in discrete-time analog computations
Neural Computation
Pulsed neural networks
On the complexity of learning for spiking neurons with temporal coding
Information and Computation
Computational Work and Time on Finite Machines
Journal of the ACM (JACM)
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
Derandomizing Approximation Algorithms Based on Semidefinite Programming
SIAM Journal on Computing
Introduction to Circuit Complexity: A Uniform Approach
Introduction to Circuit Complexity: A Uniform Approach
Models of Computation: Exploring the Power of Computing
Models of Computation: Exploring the Power of Computing
Advances in Algorithms, Languages, and Complexity
Advances in Algorithms, Languages, and Complexity
Theoretical Advances in Neural Computation and Learning
Theoretical Advances in Neural Computation and Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On computing Boolean functions by a spiking neuron
Annals of Mathematics and Artificial Intelligence
A theory of complexity for continuous time systems
Journal of Complexity
Computational complexity of neural networks: a survey
Nordic Journal of Computing
The Power of Approximation: A Comparison of Activation Functions
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Computing with Almost Optimal Size Neural Networks
Advances in Neural Information Processing Systems 5, [NIPS Conference]
On Small Depth Threshold Circuits
SWAT '92 Proceedings of the Third Scandinavian Workshop on Algorithm Theory
On Small Depth Threshold Circuits
SWAT '92 Proceedings of the Third Scandinavian Workshop on Algorithm Theory
On The Computational Power of Probabilistic and Faulty Neural Networks
ICALP '94 Proceedings of the 21st International Colloquium on Automata, Languages and Programming
SOFSEM '95 Proceedings of the 22nd Seminar on Current Trends in Theory and Practice of Informatics
The Computational Power of Continuous Time Neural Networks
SOFSEM '97 Proceedings of the 24th Seminar on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
Continuous-time symmetric Hopfield nets are computationally universal
Neural Computation
Descartes' rule of signs for radial basis function neural networks
Neural Computation
Computation: finite and infinite machines
Computation: finite and infinite machines
A Model for Fast Analog Computation Based on Unreliable Synapses
Neural Computation
On the Computational Complexity of Binary and Analog Symmetric Hopfield Nets
Neural Computation
On the Computational Power of Winner-Take-All
Neural Computation
Analog versus discrete neural networks
Neural Computation
The computational power of discrete hopfield nets with hidden units
Neural Computation
Lower bounds for the computational power of networks of spiking neurons
Neural Computation
Computational power of neural networks: a characterization in terms of Kolmogorov complexity
IEEE Transactions on Information Theory
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Complexity of reachability problems for finite discrete dynamical systems
Journal of Computer and System Sciences
Awaking and sleeping of a complex network
Neural Networks
Predecessor existence problems for finite discrete dynamical systems
Theoretical Computer Science
Hopfield Network as Static Optimizer: Learning the Weights and Eliminating the Guesswork
Neural Processing Letters
2008 Special Issue: Some neural networks compute, others don't
Neural Networks
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
An extended class of multilayer perceptron
Neurocomputing
Sequential triangle strip generator based on hopfield networks
Neural Computation
Energy complexity and depth of threshold circuits
FCT'09 Proceedings of the 17th international conference on Fundamentals of computation theory
Energy and depth of threshold circuits
Theoretical Computer Science
Size-energy tradeoffs for unate circuits computing symmetric Boolean functions
Theoretical Computer Science
Maintaining real-time synchrony on SpiNNaker
Proceedings of the 8th ACM International Conference on Computing Frontiers
Optimal triangle stripifications as minimum energy states in hopfield nets
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A variational formulation for the multilayer perceptron
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Reversible iterative graph processes
Theoretical Computer Science
A biologically realizable bayesian computation in a cortical neural network
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Energy-efficient threshold circuits computing mod functions
CATS '11 Proceedings of the Seventeenth Computing: The Australasian Theory Symposium - Volume 119
Energy-efficient threshold circuits computing mod functions
CATS 2011 Proceedings of the Seventeenth Computing on The Australasian Theory Symposium - Volume 119
Complexity of counting output patterns of logic circuits
CATS '13 Proceedings of the Nineteenth Computing: The Australasian Theory Symposium - Volume 141
Clock power minimization using structured latch templates and decision tree induction
Proceedings of the International Conference on Computer-Aided Design
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We survey and summarize the literature on the computational aspects of neural network models by presenting a detailed taxonomy of the various models according to their complexity theoretic characteristics. The criteria of classification include the architecture of the network (feedforward versus recurrent), time model (discrete versus continuous), state type (binary versus analog), weight constraints (symmetric versus asymmetric), network size (finite nets versus infinite families), and computation type (deterministic versus probabilistic), among others. The underlying results concerning the computational power and complexity issues of perceptron, radial basis function, winner-take-all, and spiking neural networks are briefly surveyed, with pointers to the relevant literature. In our survey, we focus mainly on the digital computation whose inputs and outputs are binary in nature, although their values are quite often encoded as analog neuron states. We omit the important learning issues.