Alberto Antonietti

Learn More
Several studies showed that people presented with source information fail to apply it to an analogous target problem unless they are instructed to use the source. Seven experiments were carried out to assess whether such a lack of spontaneous transfer occurs because individuals do not activate the source during the target task or because they do not realize(More)
The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real(More)
The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into(More)
GOAL In this study, we defined a realistic cerebellar model through the use of artificial spiking neural networks, testing it in computational simulations that reproduce associative motor tasks in multiple sessions of acquisition and extinction. METHODS By evolutionary algorithms, we tuned the cerebellar microcircuit to find out the near-optimal(More)
The cerebellum plays a crucial role in sensorimotor control and cerebellar disorders compromise adaptation and learning of motor responses. However, the link between alterations at network level and cerebellar dysfunction is still unclear. In principle, this understanding would benefit of the development of an artificial system embedding the salient(More)
The human nervous system and its functioning have always been the focus of attention for many scientists for centuries and they were studied from many different points of view. This research project bridges neuroscience, neurocomputing and bioengineering and aims to explore the brain neural mechanisms with two approaches: modeling and experimental. Since(More)
The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was(More)
Since the Marr-Albus model, computational neuroscientists have been developing a variety of models of the cerebellum, with different approaches and features. In this work, we developed and tested realistic artificial Spiking Neural Networks inspired to this brain region. We tested in computational simulations of the Vestibulo-Ocular Reflex protocol three(More)
The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. A biological inspired cerebellar model with distributed plasticity has been embedded into a real-time controller of a neurorobot. A cerebellum-driven task has been designed: the Vestibulo-Ocular Reflex (VOR), which produces eye movements stabilizing images on the(More)
  • 1