Dynamic Redistribution of Plasticity in a Cerebellar Spiking Neural Network Reproducing an Associative Learning Task Perturbed by TMS

Abstract

During natural learning, synaptic plasticity is thought to evolve dynamically and redistribute within and among subcircuits. This process should emerge in plastic neural networks evolving under behavioral feedback and should involve changes distributed across multiple synaptic sites. In eyeblink classical conditioning (EBCC), the cerebellum learns to predict the precise timing between two stimuli, hence EBCC represents an elementary yet meaningful paradigm to investigate the cerebellar network functioning. We have simulated EBCC mechanisms by reconstructing a realistic cerebellar microcircuit model and embedding multiple plasticity rules imitating those revealed experimentally. The model was tuned to fit experimental EBCC human data, estimating the underlying learning time-constants. Learning started rapidly with plastic changes in the cerebellar cortex followed by slower changes in the deep cerebellar nuclei. This process was characterized by differential development of long-term potentiation and depression at individual synapses, with a progressive accumulation of plasticity distributed over the whole network. The experimental data included two EBCC sessions interleaved by a trans-cranial magnetic stimulation (TMS). The experimental and the model response data were not significantly different in each learning phase, and the model goodness-of-fit was [Formula: see text] for all the experimental conditions. The models fitted on TMS data revealed a slowed down re-acquisition (sessions-2) compared to the control condition ([Formula: see text]). The plasticity parameters characterizing each model significantly differ among conditions, and thus mechanistically explain these response changes. Importantly, the model was able to capture the alteration in EBCC consolidation caused by TMS and showed that TMS affected plasticity at cortical synapses thereby altering the fast learning phase. This, secondarily, also affected plasticity in deep cerebellar nuclei altering learning dynamics in the entire sensory-motor loop. This observation reveals dynamic redistribution of changes over the entire network and suggests how TMS affects local circuit computation and memory processing in the cerebellum.

Computational Modelling of Cerebellar Magnetic Stimulation: The Effect of Washout

“…However, due to the noninvasive nature of TMS investigation, it was possible to only speculate about the putative mechanisms underlying the behavioural differences. To have a greater insight about the neural processes involved by the TMS, a computational approach was used in two previous works [1,3].…”

“…Recently, a detailed spiking neural network model of the cerebellar microcircuit proved able to reproduce multiple cerebellar-driven tasks [5], among which the EBC paradigm [2]. Having been validated, the SNN model was challenged to fit the two experimental datasets recorded by Monaco and colleagues from human subjects [13,14], with a long [1] or a short washout [3]. In both studies, the SNN model was able to capture the specific alterations in the second EBC session caused by TMS interference.…”

Estimation of the Motor Threshold for Near‐Rectangular Stimuli Using the Hodgkin–Huxley Model

“…e HH equations have been widely used in the modeling of several neuromodulation modalities and neuronal behavior [12][13][14]. In this study, for the computational modeling of cortical neurons, a Hodgkin-Huxley type model is selected, including four conductances (the voltage-dependent sodium, potassium, slow potassium, and resting (leak) membrane conductance) and the TMS-induced current density.…”

A Basal Ganglia Computational Model to Explain the Paradoxical Sensorial Improvement in the Presence of Huntington’s Disease

“…In recent decades, the use of biologically inspired computational models emulating spiking neural networks 17 has been demonstrated as being useful for understanding experimental recordings from multiple brain areas 18,19 and for studying different neurological alterations. [20][21][22] Thus, computational models represent a promising approach to explore not only the normal operation of the BG, but also how different artificial alterations (e.g. levodopa) or diseases (e.g.…”