Oscillatory and bursting properties of neurons
The handbook of brain theory and neural networks
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
A MOSFET-based model of a class 2 nerve membrane
IEEE Transactions on Neural Networks
Mathematical-model-based design of silicon burst neurons
Neurocomputing
Evolution of analog circuit models of ion channels
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
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The two major principles in silicon neuron implementations are phenomenological and conductance-based. The former reproduces some properties perceived by the designers and does not claim mechanisms are consistent. The latter reproduces the dynamics of the ion channels on the nerve membranes. Although it makes the silicon neurons more similar to biological ones, the implementations tend to be complicated because it attempts to replicate the detailed dynamics of the biological components. In previous work [1], we proposed a simple and biologically realistic MOSFET-based Class 2 silicon nerve membrane. It reproduced basic mathematical structures that produce resting potential and threshold in the Hodgkin-Huxley equations [2,3]. In this paper, we focus on a method of designing such a silicon nerve membrane, which is based on mathematical analyses that have been applied to biological neuron models. The concept of the method is to reproduce the topological structure in phase portraits of biological nerve membrane models utilizing the characteristic curves of basic MOSFET circuitries as the elements of these for silicon nerve membranes. The design method also revealed how to tune their parameters up.