Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning

Abstract

According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones–more likely depression, upbound microzones—more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.

Purkinje cell models: past, present and future

“…It has been demonstrated experimentally that different cerebellar zones undergo different opposite directions of plasticity (De Zeeuw, 2021 ). A recently published olivocerebellar network model incorporated bidirectional plasticity in cerebellar learning by embedding upbound (mainly Z+ PCs) and downbound (mainly Z- PCs) zones that have different plasticity rules (Geminiani et al, 2024 ). This model showed that plasticity can regulate the cascade of precise spiking patterns spread throughout the CC and DCN.…”

Spiking network model of the cerebellum as a reinforcement learning machine

“…The network successfully performed SL and control tasks but failed to solve RL and complex recognition tasks. Geminiani et al (2024) implemented a spiking network-based cerebellar model featuring bidirectional plasticity at PF-PC and PF-molecular layer interneurons (MLIs) synapses across multiple microzones, with several microzones more likely depression and others more likely potentiation during motor control. Their results highlight the cerebellum's capacity for error correction and adaptive learning, particularly within the context of associative learning and motor control.…”

“…These findings have been extending the Marr-Albus-Ito theory gradually (Fujita, 2016; Raymond and Medina, 2018; Yamazaki, 2021). Furthermore, the learning theory has been examined by realistic spiking network models of the cerebellum (Medina et al, 2000; Gosui and Yamazaki, 2016; Hausknecht et al, 2017; Abadía et al, 2021; Kuriyama et al, 2021; Shinji et al, 2024; Geminiani et al, 2024). Especially, Hausknecht et al (2017) examined the learning capability of the cerebellum through a cerebellar spiking network modeled in an SL context.…”