Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell

Abstract

Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy and thus prevent these models from being part of brain-area models including thousands or even millions of neurons. The cerebellar input layer represents a canonical example of large scale networks. In particular, the cerebellar granule cells, the most numerous cells in the whole mammalian brain, have been proposed as playing a pivotal role in the creation of somato-sensorial information representations. Enhanced burst frequency (spiking resonance) in the granule cells has been proposed as facilitating the input signal transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell feature in the operation of the granular layer remains largely unclear. This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency whilst capturing essential aspects of single-neuron processing. Therefore, we selected a light computational neuron model template (the adaptive-exponential integrate-and-fire model), whose parameters were progressively refined using an automatic parameter tuning with evolutionary algorithms (EAs). The resulting point-neuron models are suitable for reproducing the main firing properties of a realistic granule cell from electrophysiological measurements, including the spiking resonance at the theta-frequency band, repetitive firing according to a specified intensity-frequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly, the proposed model also reproduced some other emergent properties (namely, silent at rest, rheobase and negligible adaptation under depolarizing currents) even though these properties were not set in the EA as a target in the fitness function (FF), proving that these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are appropriate for future research of the functional implication of bursting resonance at the theta band in large-scale granular layer network models.

A Comparative Study of Stochastic Optimizers for Fitting Neuron Models. Application to the Cerebellar Granule Cell

“…This fact has been supported by several comparisons with detailed models and experimental recordings (Brette and Gerstner, 2005;Nair et al, 2014;Naud et al, 2008). As part of their method, Marín et al (2020) tuned the parameters of the AdEx model to fit the neuronal spiking dynamics of real recordings. In this context, the authors model the tuning procedure as an optimization problem and study different objective functions to conduct the optimization.…”

“…It is defined in (2) as an abstract function that depends on the ten model parameters included in Table 1. This configuration is that tagged as FF4 in Marín et al (2020), and it is further explained below.…”

“…According to Marín et al (2020), the initial membrane potential starts with the same value as the leak reversal potential, i.e. V init = E L .…”

“…Previous work by Marín et al (2020) proposed a methodology for building computationally efficient neuron models of cerebellar GrCs that replicate some inherent properties of the biological cell. Since the cerebellar GrCs show compact and simple morphology (D'Angelo et al, 2001;Delvendahl et al, 2015), it is appropriate to consider a monocompartment model.…”

“…This compound optimizer will be referred to as MSASS. The last method, which will be called MemeGA, is an ad-hoc memetic algorithm (Cruz et al, 2018;Marić et al, 2014) that results from replacing the mutation part of the genetic method by Marín et al (2020) by the referred local search procedure, SASS.…”