Point-neuron models of the cerebellum, the role of distributed plasticity explained by closed-loop experiments

The cerebellar circuit is actively involved in sensorimotor control and adaptation. During natural learning, synaptic plasticity in the cerebellum is thought to evolve dynamically and redistribute within and among subcircuits. This process should emerge in plastic neural networks developing under behavioral feedback and should involve changes distributed across multiple synaptic sites. We have reconstructed a realistic point-neuron cerebellar model, simplifying biophysically detailed neuronal models, and we embedded multiple plasticity rules imitating those revealed experimentally. A single model was able to drive learning in various paradigms, expressing a complex repertoire of responses. The model was then tuned to fit experimental data, estimating the underlying learning time-constants. This process was characterized by a differential development of long-term potentiation and depression at individual synapses, with a progressive accumulation of plasticity distributed over the whole network. Importantly, the model was also able to capture the alterations caused by cerebellar Transcranial Magnetic Stimulation (TMS). 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.