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Computational capacity of a cerebellum model

par De Gernier, Robin ;Solinas, Sergio;Rössert, Christian;Mapelli, Jonathan;Haelterman, Marc ;Massar, Serge
Référence 26th International Conference on Artificial Neural Networks, ICANN 2017(11 September 2017 through 14 September 2017: Alghero; Italy), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, Vol. 10613, page (425-426)
Publication Publié, 2017

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Résumé :

Linking network structure to function is a long standing issuein the neuroscience field. An outstanding example is the cerebellum. Itsstructure was known in great detail for decades but the full range ofcomputations it performs is yet unknown. This reflects a need for newsystematic methods to characterize the computational capacities of thecerebellum. In the present work, we apply a method borrowed from thefield of machine learning to evaluate the computational capacity and theworking memory of a prototypical cerebellum model.The model that we study is a reservoir computing rate model ofthe cerebellar granular layer in which granule cells form a recurrentinhibitory network and Purkinje cells are modelled as linear trainablereadout neurons. It was introduced by [2, 3] to demonstrate how therecurrent dynamics of the granular layer is needed to perform typicalcerebellar tasks (e.g. : timing-related tasks).The method, described in detail in [1], consists in feeding the modelwith a random time dependent input signal and then quantifying howwell a complete set of functions (each function representing a differenttype of computation) of the input signal can be reconstructed by taking alinear combination of the neuronal activations. We conducted simulationswith 1000 granule cells. Relevant parameters were optimized within a bio-logically plausible range using a Bayesian Learning approach. Our resultsshow that the cerebellum prototypical model can compute both linearfunctions - as expected from previous work -, and - surprisingly - highlynonlinear functions of its input (specifically, up to the 10th degree Legen-dre polynomial functions). Moreover, the model has a working memoryof the input up to 100 ms in the past. These two properties are essen-tial to perform typical cerebellar functions, such as fine-tuning nonlinearmotor control tasks or, we believe, even higher cognitive functions.