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Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning

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Abstract

The cerebellum is involved in learning and memory of sensory motor skills. However, the way this process takes place in local microcircuits is still unclear. The initial proposal, casted into the Motor Learning Theory, suggested that learning had to occur at the parallel fiber–Purkinje cell synapse under supervision of climbing fibers. However, the uniqueness of this mechanism has been questioned, and multiple forms of long-term plasticity have been revealed at various locations in the cerebellar circuit, including synapses and neurons in the granular layer, molecular layer and deep-cerebellar nuclei. At present, more than 15 forms of plasticity have been reported. There has been a long debate on which plasticity is more relevant to specific aspects of learning, but this question turned out to be hard to answer using physiological analysis alone. Recent experiments and models making use of closed-loop robotic simulations are revealing a radically new view: one single form of plasticity is insufficient, while altogether, the different forms of plasticity can explain the multiplicity of properties characterizing cerebellar learning. These include multi-rate acquisition and extinction, reversibility, self-scalability, and generalization. Moreover, when the circuit embeds multiple forms of plasticity, it can easily cope with multiple behaviors endowing therefore the cerebellum with the properties needed to operate as an effective generalized forward controller.

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Notes

  1. Notes on the nature of models used to test the learning rules

    In order to test the impact of the plasticity rules, they have been coupled to simplified cerebellar models [23] C. Casellato, A. Antonietti, J.A. Garrido, R.R. Carrillo, N.R. Luque, E. Ros, A. Pedrocchi, and E. D’Angelo [4]. Adaptive robotic control driven by a versatile spiking cerebellar network. PLoS One 9, e112265, [105] C. Casellato, A. Antonietti, J.A. Garrido, G. Ferrigno, E. D’Angelo, and A. Pedrocchi (2015). Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks. Front Comput Neurosci 9, 24. In the spiking cerebellar models, neurons are of the “integrate-and-fire” type, i.e., they have an RC membrane charging mechanism and a threshold for firing. The main properties of these neurons are to generate a linear frequency-intensity relationship in response to currents injected by synaptic inputs, to have a resting membrane potential or a basal firing frequency similar to real cells, and to show variations in firing during task processing reflecting the value ranges observed in vivo. Synaptic activation occurs through current injection into the model neurons and inputs from various neurons are integrated over the RC.

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Authors and Affiliations

  1. Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

    Egidio D’Angelo, Lisa Mapelli, Jesus A. Garrido & Francesca Prestori

  2. Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy

    Egidio D’Angelo & Jessica Monaco

  3. Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Rome, Italy

    Lisa Mapelli

  4. Department of Computer Architecture and Technology, University of Granada, Granada, Spain

    Jesus A. Garrido, Niceto Luque & Eduardo Ros

  5. Department of Bioengineering, Politecnico of Milano, Milano, Italy

    Claudia Casellato & Alessandra Pedrocchi

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  1. Egidio D’Angelo
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D’Angelo, E., Mapelli, L., Casellato, C. et al. Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning. Cerebellum 15, 139–151 (2016). https://doi.org/10.1007/s12311-015-0711-7

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