Traditional models of neurons are based on the assumption that a synapse is a lumped element represented by a scalar synaptic weight. But to faithfully model biological neurons, synapse is considered as a linear filter. Thus, a new model of continuous time neuron is discussed. It is described how such model leads to interesting neural networks. Also continuous time, complex-valued neuron is discussed. It is also described, how a synapse can be modeled as an FIR filter. Such a model of neuron leads to practically useful neural networks. A novel, continuous time associative memory is proposed. An approach to resolve the convergence of state of such an associative memory is discussed. Various interesting generalizations of neural networks are described.
CITATION STYLE
Garimella, R. (2006). Some novel real/complex-valued neural network models. In Computational Intelligence, Theory and Applications: International Conference 9th Fuzzy Days in Dortmund, Germany, Sept. 18-20, 2006 Proceedings (pp. 473–483). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-34783-6_47
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