"Computational models in neuroscience can be used to predict causal
relationships between biological mechanisms in neurons and networks,
such as the effect of blocking an ion channel or synaptic connection
on neuron activity. Since developing a biophysically realistic, single
neuron model is exceedingly difficult, software has been developed for
automatically adjusting parameters of computational neuronal
models. The ideal optimization software should work with commonly used
neural simulation software; thus, we present software which works with
models specified in declarative format for the MOOSE
simulator. Experimental data can be specified using one of two
different file formats. The fitness function is customizable as a
weighted combination of feature differences. The optimization itself
uses the covariance matrix adaptation-evolutionary strategy, because
it is robust in the face of local fluctuations of the fitness
function, and deals well with a high-dimensional and discontinuous
fitness landscape. We demonstrate the versatility of the software by
creating several model examples of each of four types of neurons (two
subtypes of spiny projection neurons and two subtypes of globus
pallidus neurons) by tuning to current clamp data.
..."
Reference:
1 .
Jedrzejewski-Szmek Z, Abrahao KP, Jedrzejewska-Szmek J, Lovinger DM, Blackwell KT (2018) Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes. Front Neuroinform 12:47 [PubMed]
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