ReConvPy: Modeling Local Field Potentials of Cerebellum Granule Neurons using Repetitive Convolutions in Python

Abstract One of the main goals in today’s computational neuroscience laboratories is the ability to construct ensemble population responses from bottom-up reconstructions that involve computationally-detailed single neuron models. As a modeler’s tool, local field potentials are population responses that abstract information from an ensemble of neurons connecting single neuron response to a circuit function. In this paper, we have developed a new implementation of reproducing the local field potentials (LFP) using the repetitive convolution technique in Python adding on to the tool set library already developed for mathematically modeling cerebellar local field potentials. The LFP tool accurately reproduces the in vitro negative N2a, N2b waves and in vivo T and C waves generated by 200 to 700 cerebellar granule neurons and replicates pharmacological changes and induced plasticity properties.