The Anatomical, physiological and computational principles of adaptive learning in the cerebellum: the micro and macrocircuits of the brain

Zucca, Riccardo

The Anatomical, physiological and computational principles of adaptive learning in the cerebellum: the micro and macrocircuits of the brain

Document Type

Doctoral thesis
Published Version

Author

Zucca, Riccardo

Director

Verschure, Paul F. M. J.

Publication Date

2015-02-13

Pages

185 p.

License

Citation

Zucca, R. The Anatomical, physiological and computational principles of adaptive learning in the cerebellum: the micro and macrocircuits of the brain. Universitat Pompeu Fabra; 2015. handle: http://hdl.handle.net/10803/286228

This citation was generated automatically.

Doctoral program

Programa de doctorat en Tecnologies de la Informació i les Comunicacions

Abstract

The human brain is undoubtedly the most complex product of evolution. Understanding how complex behaviour is generated by the intricacy of hundred billion of neurons and synapses fascinated scientists and philosophers for millennia. The multiscale trait of the central nervous system is a hallmark of its architecture and brain functions emerge from the interaction of its components at di erent temporal and spatial scales. A full understanding cannot be achieved unless we approach this complexity at these di erent scales, with techniques that are sensitive to these various levels of organization. Here we propose a convergent approach to scale up from local to global organization of the brain that relies on experimental, computational and behavioral methods, mainly focusing on the cerebellum and the neocortex. Through electrophysiological, neuro-prosthetic and behavioral studies on a reduced animal preparation, we provide further evidence about the central role of the climbing bre signal in precisely modulating the overall activity and ne{tuning the learning process in a basic functional cerebellar microcircuit. Having identi ed the properties of a single microcircuit, how could the computational principles be extended to a larger scale that includes also the polysynaptic connectivity with the neocortex? To tackle this question, we propose a computational approach that integrates reconstruction of anatomical structural data of the neocortex with biophysical neuronal dynamics, that we employed to infer patterns of neuronal activation in healthy and simulated disease. However, the brain operates vii in a natural environment that is continuously evolving. To reconcile the reductionist approach with the real demands of an operating brain, while maintaining a high degree of control, we propose an hybrid approach that mixes virtual{reality with wearable devices that we validated in a conditioning task. We show that such approach can overcome the limitations of the classical laboratory settings thus providing a more ecological framework to infer functional principles. Altogether, this thesis work advances our understanding of the cerebellar mechanisms involved during the acquisition of adaptive motor behaviors. Moreover, it paves the way for using a convergence of computational and experimental approaches that o er complementary views of brain organization to address questions about functions in health and disease, which cannot be reduced to a single observational scale or method.