Journal of Global Optimization
Validation of Knowledge-Based Systems by Means of Stochastic Search
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
The NEURON Book
Automated neuron model optimization techniques: a review
Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons
Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
An exponential-decay synapse integrated circuit for bio-inspired neural networks
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
IEEE Transactions on Neural Networks
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We propose a new estimation method for the characterization of the Hodgkin-Huxley formalism. This method is an alternative technique to the classical estimation methods associated with voltage clamp measurements. It uses voltage clamp type recordings, but is based on the differential evolution algorithm. The parameters of an ionic channel are estimated simultaneously, such that the usual approximations of classical methods are avoided and all the parameters of the model, including the time constant, can be correctly optimized. In a second step, this new estimation technique is applied to the automated tuning of neuromimetic analog integrated circuits designed by our research group. We present a tuning example of a fast spiking neuron, which reproduces the frequency-current characteristics of the reference data, as well as the membrane voltage behavior. The final goal of this tuning is to interconnect neuromimetic chips as neural networks, with specific cellular properties, for future theoretical studies in neuroscience.