|dc.description.abstract||Our research activity investigates the computational processes underlying the execution of complex
sequences of movements and aims at understanding how different levels of the nervous system
interact and contribute to the gradual improvement of motor performance during learning.
Many research areas, from neuroscience to engineering, investigate, from different perspectives and
for diverse purposes, the processes that allow humans to efficiently perform skilled movements.
From a biological point of view, the execution of voluntary movements requires the interaction
between nervous and musculoskeletal systems, involving several areas, from the higher cortical
centers to motor circuits in the spinal cord.
Understanding these interactions could provide important insights for many research fields, from
machine learning to medicine, from the design of robotic limbs to the development of new
treatments for movement disorders, such as Parkinson’s disease.
This goal could be achieved by finding an answer to the following questions:
· How does the central nervous system control and coordinate natural voluntary movements?
· Which brain areas are involved in learning a new motor skill? What are the changes that
happen in these neural structures? What are the aspects of the movement memorized?
· Which is the process that allows people to perform a skilled task, such as playing an
instrument, being apparently unaware of the movements they are performing?
· What happen when a neurodegenerative disease affects the brain areas involved in executing
These questions have been addressed from different perspectives and levels of analysis, from the
exploration of the anatomical structure of the neural systems thought to be involved in motor
learning (such as the basal ganglia, cerebellum and hippocampus) to the investigation of their neural
interaction; from the analysis of the activation of these systems in executing a motor task to the
specific activation of a single or a small group of neurons within them. In seeking to understand all
the breadth and facets of motor learning, many researchers have used different approaches and
methods, such as genetic analysis, neuroimaging techniques (such as fMRI, PET and EEG), animal
models and clinical treatments (e.g. drugs administration and brain stimulation).
These studies have provided a large body of knowledge that has led to several theories related to the
role of the central nervous system in controlling and learning simple and complex movements.
These theories envisage the interaction among multiple brain regions, whose cooperation leads to
the execution of skilled movements.
How can we test these interactions for the purpose of evaluating a theory?
Our answer to this question is investigating these interactions through computational models, which
provide a valuable complement to the experimental brain research, especially in evaluating the
interactions within and among multiple neural systems.
Based on these concepts arises our research, which addresses the questions previously pointed out
and aims at understanding the computational processes performed by two neural circuits, the Basal
Ganglia and Cerebellum, in motor learning.
We propose a new hypothesis about the neural processes occurring during acquisition and retention
of novel motor skills.
According to our hypothesis, a sequence of movements is stored in the nervous system in the form
of a spatial sequence of points (composing the trajectory plan associated to the motor sequence) and
a sequence of motor commands.
We propose that learning novel motor skills requires two phases, in which two different processes
Early in learning, when movements are slower, less accurate, and attention demanding, the motor
sequence is performed by converting the sequence of target points into the appropriate
sequence of motor commands. During this phase, the trajectory plan is acquired and the movements
rely on the information provided by the visuo-proprioceptive feedback, which allows to correct the
sequence of movements so that the actual trajectory plan corresponds to the desired one and the
lowest energy is spent by the muscular subsystem involved.
During the late learning phase, when the sequence of movements is performed faster and
automatically, with little or no cognitive resources needed to complete it, and is characterized by
anticipatory movements, the sequence of motor commands is acquired and thus, the sequence of
movements comes to be executed as a single behavior.
We suggest that the Basal Ganglia and Cerebellum are involved in learning novel motor sequences,
although their role is crucial in different stages of learning.
Accordingly, we propose a neural scheme for procedural motor learning, comprising the basal
ganglia, cerebellum and cortex, which envisages that the basal ganglia, interacting with the cortex,
select the sequence of target points to reach (composing the trajectory plan), whereas the
cerebellum, interacting with the cortex, is responsible for converting the trajectory plan into the
appropriate sequence of motor commands.
Consequently, we suggest that early in learning, task performance is more dependent on the
procedural knowledge maintained by the cortex-basal ganglia system, while after a long-term
practice, when the sequence of motor commands is acquired within the cerebellum, task
performance is more dependent on the motor command sequence maintained by the cortexcerebellar
We tested the neural scheme (and the hypothesis behind it) through a computational model that
incorporates the key anatomical, physiological and biological features of these brain areas in an
integrated functional network.
Analyzing the behavior of the network in learning novel motor tasks and executing well-known
motor tasks, both in terms of the neural activations and motor response provided, we found that the
results obtained fit those reported by many neuroimaging and experimental studies presented in the
We also carried out further experiments, simulating neurodegenerative disorders (Parkinson's and
Huntington disease, which affect the basal ganglia) and cerebellar damages. Results obtained by
these experiments validates the proposed hypothesis, showing that the basal ganglia play a key role
during the early stage of learning, whereas the cerebellum is crucial for motor skill retention.
Our model provides some insights about the learning mechanisms occurring within the cerebellum
and gains further understanding of the functional dynamics of information processing within the
basal ganglia and cerebellum in normal as well as in diseased brains. Therefore the model provides
novel predictions about the role of basal ganglia and cerebellum in motor learning, motivating
further investigations of their interactions. [edited by author]||en_US