Simulation of nervous centres in closed-loop of perception-action

Abstract

The human brain, thanks to the evolution, has developed efficient biological structures able to perform a wide range of complex tasks. This is also the case of one of its centres: the cerebellum. This structure plays a fundamental role in different motor control tasks such as coordination of movements or calibration of sensorimotor relationship. In this thesis computational models of the cerebellum have been developed in order to achieve better understanding of the biological mechanisms that confer the cerebellum its motor control and learning capabilities. Simulated and real robots have been used in this work as emulated bodies to control. Thus we aim to validate several hypotheses about the cerebellum operation when performing different motor control tasks such as object manipulation, eye blink classical conditioning (EBCC) or vestibulo-ocular reflex (VOR) experiments. The scope of this thesis is the development of biologically inspired control systems based in cerebellar models able to perform different motor control tasks using biomorphic robots in real time. This work can be subdivided in three main blocks: (i) development of all the tools needed for this study, (ii) development of a cerebellar model based in data obtained with biological experiments, and (iii) validation of the cerebellar models embedding these ones in control schemes able to perform different motor control tasks with biomorphic robots in real time. An upgraded version of the EDLUT simulator has been used as the main simulation tool for this study. EDLUT is an efficient spiking neural network simulator developed by our research group at the University of Granada. It was conceived as a small tool capable of efficiently simulate medium-scale spiking neural networks. In this thesis we have systematically improved the efficiency and functionality of EDLUT. We have parallelized its simulation in multicore CPUGPU co-processing architectures, developed new and efficient event-driven and time-driven simulation methods and implemented new neuron models and learning rules. Additionally, we have included new modules and features in EDLUT related with the robotic control in real time. EDLUT-simulation spiking neural models can now connect with many (simulated or real) robotic devices using TCP/IP connections. Communication interfaces able to translate the cerebellar signals (spikes) in robotic signal (analogical signals) and vice versa have also been implemented within EDLUT. Finally, EDLUT incorporates a real time supervisor able to ensure that a simulation is performed in real time. Thus, EDLUT is now more than a simple spiking neural network simulator. It is a simulation tool able to create biologically inspired control schemes based in spiking neural networks to perform different motor control tasks using biomorphic robots in real time. Starting from a cerebellar model previously developed by our research group, two new plasticity mechanisms at deep cerebellar nuclei (DCN) level have been proposed and implemented, conferring to the cerebellar model with learning consolidation and gain adaptation capabilities. We have also propose a new neural model for Purkinje cells able to replicate its tri-modal spike modes (tonic, silence and bursting) and a new Inferior Olive (IO) layer interconnected with electrical coupling able to better codify the error signal. Finally, a new synaptic connection from the IO to the DCN cells have been proposed and included. All these new elements, based in theoretical hypotheses and experimental results in the literature, have increased the biological plausibility of our cerebellar model. Finally we have analysed how each one of the above-mentioned elements affects the behaviour of the whole cerebellar model when performing motor control experiments as a test-bench: a manipulation object task with a robotic arm, an EBCC experiment with a simulated environment or a VOR experiment with a robotic head.