Despite much evidence of the existence of a strong interaction between the cerebellum and the medial prefrontal cortex (mPFC) during trace eye blinking conditioning (TEBC)), the neural mechanisms underlying this interaction are still unclear. We propose a neurophysiologically plausible computational model to address this issue. The model architecture is constrained on the basis of the relevant known system-level anatomy of the cerebellar-cortical circuits. Its physiology reproduces the effects on plasticity mechanisms of granule cells time sensitivity according to which different subpopulations are active at different moments during conditioned stimuli. The computer simulations run with the model suggest that these features are critical to understand how the cooperation between cerebellum and mPFC supports motor areas during TEBC, and to study the relationship between the trace interval size and the involvement of mPFC in TEBC. The model highlights how the greater trace interval implies the greater involvement of mPFC. While short trace intervals lead to M1 activation with very low involvement of the mPFC. The model was validated by reproducing the data collected through recent experiments on real mice. To build the system-level architecture of the model, we used PyNEST, the Python programming language interface to Spiking Neuron Simulation Tool − NEST

How the cerebellum and prefrontal cortex cooperate during associative learning

Pierandrea Mirino
;
2021

Abstract

Despite much evidence of the existence of a strong interaction between the cerebellum and the medial prefrontal cortex (mPFC) during trace eye blinking conditioning (TEBC)), the neural mechanisms underlying this interaction are still unclear. We propose a neurophysiologically plausible computational model to address this issue. The model architecture is constrained on the basis of the relevant known system-level anatomy of the cerebellar-cortical circuits. Its physiology reproduces the effects on plasticity mechanisms of granule cells time sensitivity according to which different subpopulations are active at different moments during conditioned stimuli. The computer simulations run with the model suggest that these features are critical to understand how the cooperation between cerebellum and mPFC supports motor areas during TEBC, and to study the relationship between the trace interval size and the involvement of mPFC in TEBC. The model highlights how the greater trace interval implies the greater involvement of mPFC. While short trace intervals lead to M1 activation with very low involvement of the mPFC. The model was validated by reproducing the data collected through recent experiments on real mice. To build the system-level architecture of the model, we used PyNEST, the Python programming language interface to Spiking Neuron Simulation Tool − NEST
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1656170
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