A MODEL OF NEURAL CIRCUIT WITH ADAPTION AND NONLINEAR CONNECTION

Based on physiological properties of synapse, soma and axon, this paper presents and analyses a model of neural circuit which can approximately simulate input-output relation, strength-duration curve, adaption and nonlinear connection of real neuron. The obtained results show that the model approximates to realistic principles of neural computation better than the available neural networks. The impulse-coded WTA(winner takes all) networks constructed with the above model find the winner more effectively than the analog WTA. Finally, the two important concepts: time competition and strength competition are introduced, which illustrate that the model has abilities to perform series and parallel information processing.

A model of neural circuit with adaption and nonlinear connection

Based on physiological properties of synapse, soma and axon, this paper presents and analyses a model of neural circuit which can approximately simulate input-output relation, strength-duration curve, adaption and nonlinear connection of real neuron. The obtained results show that the model approximates to realistic principles of neural computation better than the available neural networks. The impulse-coded WTA(winner takes all) networks constructed with the above model find the winner more effectively than the analog WTA. Finally, the two important concepts: time competition and strength competition are introduced, which illustrate that the model has abilities to perform series and parallel information processing.