Parameter Optimization Using Covariance Matrix Adaptation—Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes

Abstract

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. Optimizations reached convergence within 1,600–4,000 model evaluations (200–500 generations × population size of 8). Analysis of the parameters of the best fitting models revealed differences between neuron subtypes, which are consistent with prior experimental results. Overall our results suggest that this easy-to-use, automatic approach for finding neuron channel parameters may be applied to current clamp recordings from neurons exhibiting different biochemical markers to help characterize ionic differences between other neuron subtypes.

Separable actions of acetylcholine and noradrenaline on neuronal ensemble formation in hippocampal CA3 circuits

“…Posterior distributions were estimated using Markov Chain Monte Carlo sampling via the Metropolis-Hastings algorithm using the pymc python module. We subsequently examined the covariance structure of samples in the resulting Markov chain to investigate dependencies amongst parameters [ 65 ].…”

“…A certain degree of redundancy exists among the parameters of Tsodyks-Markram type models of short-term plasticity as multiple parameters control the scaling of amplitudes in response to a spike. To explore this, we examined the covariance structure of the posterior distribution over short-term plasticity parameters [65,66] (S3A and S3B Fig) . We observed strong correlations amongst the 'g', 'f 0 ', and 'a' parameters in our excitatory STP model, and strong correlations between the 'g', 'τ d' 'f 0 ', 'a 0 ', and 'b' parameters in the inhibitory STP model. This indicates that there were many parameter sets that could explain our results.…”

Acetylcholine boosts dendritic NMDA spikes in a CA3 pyramidal neuron model

“…To build the model with reasonable contributions of the relevant potassium channels, the conductance of each channel was optimised to best fit with experimental data (Figure 1). This was done by implementing a covariance matrix adaptation evolution strategy (Hansen, 2016; Jȩdrzejewski-Szmek et al, 2018) in which parameters were adjusted in order to match outputs from the model as closely as possible with experimental measurements of resting membrane potential and input resistance. The optimisation was implemented using the PyCMA package (https://github.com/CMA-ES/pycma).…”

Integration of heterogeneous biological data in multiscale mechanistic model calibration: application to lung adenocarcinoma

“…The calibration is carried out using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, a continuous optimizer belonging to the evolutionary methods family (Hansen et al, 2003;Jdrzejewski-Szmek et al, 2018;Tomasoni et al, 2021).…”