Parallel distributed processing: explorations in the microstructure of cognition, vol. 2: psychological and biological models
Multilayer feedforward networks are universal approximators
Neural Networks
Neural network architectures: an introduction
Neural network architectures: an introduction
Neural networks for control
Kolmogorov's theorem and multilayer neural networks
Neural Networks
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
Elements of artificial neural networks
Elements of artificial neural networks
Fast sigmoidal networks via spiking neurons
Neural Computation
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
Computing with spiking neurons
Pulsed neural networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Industrial Applications of Neural Networks
Industrial Applications of Neural Networks
Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives
Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives
Synchronous firing and higher-order interactions in neuron pool
Neural Computation
Approximation capability in C(R¯n) by multilayer feedforward networks and related problems
IEEE Transactions on Neural Networks
Existence and uniqueness results for neural network approximations
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.