48 research outputs found
Understanding upper-limb movements via neurocomputational models of the sensorimotor system and neurorobotics: where we stand
Roboticists and neuroscientists are interested in understanding and reproducing the neural and cognitive mechanisms behind the human ability to interact with unknown and changing environments as well as to learn and execute fine movements. In this paper, we review the system-level neurocomputational models of the human motor system, and we focus on biomimetic models simulating the functional activity of the cerebellum, the basal ganglia, the motor cortex, and the spinal cord, which are the main central nervous system areas involved in the learning, execution, and control of movements. We review the models that have been proposed from the early of 1970s, when the first cerebellar model was realized, up to nowadays, when the embodiment of these models into robots acting in the real world and into software agents acting in a virtual environment has become of paramount importance to close the perception-cognition-action cycle. This review shows that neurocomputational models have contributed to the comprehension and reproduction of neural mechanisms underlying reaching movements, but much remains to be done because a whole model of the central nervous system controlling musculoskeletal robots is still missing
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
25th annual computational neuroscience meeting: CNS-2016
The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong
A neurocomputational model of reaching movements
2013 - 2014How the brain controls movement is a question that has fascinated researchers from
different areas as neuroscience, robotics and psychology. To understand how we
move is not only an intellectual challenge, but it is important for finding new
strategies for nursing people with movement diseases, for rehabilitation and to
develop new robotic technology.
While there is an agreement about the role of the primary motor cortex (M1) in the
execution of voluntary movements, it is still debated what (and how) is encoded by
the neural activity of the motor cortex.
To unveil the "code" used for executing voluntary movements we investigated the
interaction between the motor cortex and the spinal cord, the main recipient of the
descending signals departing from M1 neurons. In particular, the research presented
in this thesis aims at understanding how primary motor cortex and spinal cord
cooperate to execute a reaching movement, and whether a modular organization of
the spinal cord can be exploited for controlling the movement.
On the basis of physiological studies about the primary motor cortex organization, we
have hypothesized that this brain area encodes both movement's parameters and
patterns of muscle activation.
We argue that the execution of voluntary movements results from the cooperation of
different clusters of neurons distributed in the rostral and caudal regions of primary
motor cortex, each of which represents different aspects of the ongoing movement. In
particular, kinetic aspects of movement are directly represented by the caudal part of
primary motor cortex as activations of alpha motoneurons, while kinematic aspects of
the movement are encoded by the rostral region and are translated by spinal cord
interneurons into alpha motoneurons activation. The population of corticomotoneuron
(CM) cells in the caudal part of M1 creates muscle synergies for a direct
control of muscle activity, useful to execute highly novel skills that require a direct
control of multijoint and single joint movements by the central nervous system
(CNS). On the other side, clusters of neurons in the rostral M1 are devoted to the
activation of different subpopulations of interneurons in the spinal cord organized in
functional modules. Each spinal module implements hardwired muscle synergies
regulating the activity of a subset of muscles working around one or more joints. The
way a module regulates the muscles activations is related to its structural properties.
One area recruits the hard-wired motor primitives hosted in the spinal cord as
spatiotemporal synergies, while the other one has direct access to the alpha
motoneurons and may build new synergies for the execution of very demanding
movements. The existence of these two areas regulating directly and indirectly the
muscle activity can explain the controversy about what kind of parameter is encoded
by the brain.
In order to validate our conjecture about the coexistence of an explicit representation
of both kinetic and kinematics aspects of the movement, we have developed and
implemented the computational model of the spinal cord and its connections with
supraspinal brain. The model incorporates the key anatomical and physiological
features of the neurons in the spinal cord (interneurons Ia, Ib and PN and Renshaw
cells, and their interconnections). The model envisages descending inputs coming
from both rostral and caudal M1 motor cortex and cerebellum (through the rubro- and
reticulo-spinal tracts), local inputs from both Golgi tendon organs and spindles, and
its output is directed towards alfa motoneurons, which also receive descending inputs
from the cortex and local inputs from spindles. The musculoskeletal model used in
this study is a one degree-of-freedom arm whose motion is restricted to the
extension/flexion of the elbow. The musculoskeletal model includes three muscles:
Biceps Short, Brachialis and Triceps Lateral.
Our simulations show that the CNS may produce elbow flexion movements with
different properties by adopting different strategies for the recruitment and the
modulation of interneurons and motoneurons. The results obtained using our
computational model confirm what has been hypothesized in literature: modularity
may be the organizational principle that the central nervous system exploits in motor
control. In humans, the central nervous system can execute motor tasks by recruiting
the motor primitives in the spinal cord or by learning new collections of synergies
essential for executing novel skills typical of our society.
To get more insights about how brain encodes movements and to unveil the role
played by the different areas of the brain we verified if the movement generated by
our model satisfied the trade-off between speed and accuracy predicted by the Fittsâ
law. An interesting result is that the speed-accuracy tradeoff does not follow from the
structure of the system, that is capable of performing fast and precise movements, but
arises from the strategy adopted to produce faster movements, by starting from a prelearned
set of motor commands useful to reach the target position and by modifying
only the activations of alfa motoneurons. These results suggest that the brain may use
the clusters of neurons in the rostral M1 for encoding the direction of the movement
and the clusters of CM cells in the caudal M1 for regulating the tradeoff between
speed and accuracy.
The simulation performed with our computational model have shown that the
activation of an area cannot exclude the activation of the other one but, on the
contrary, both the activations are needed to have a simulated behaviour that fits the
real behavior. [edited by Author]XIII n.s
Exploiting Stability Regions for Online Signature Verification
We present a method for finding the stability regions within a set of genuine signatures and for selecting the most suitable one to be used for online signature verification. The definition of stability region builds upon motor learning and adaptation in handwriting generation, while their selection exploits both their ability to model signing habits and their effectiveness in capturing distinctive features. The stability regions represent the core of a signature verification system whose performance is evaluated on a standard benchmark
Some Observations on Handwriting from a Motor Learning Perspective
AbstractâWe discuss the dynamics of signatures in the light of recent findings in motor learning, according to which a signature is a highly automated motor task and, as such, it is stored in the brain as both a trajectory plan and a motor plan. We then conjecture that such a stored representation does not necessarily include the entire signature, but can be limited to only parts of it, those that have been learned better and therefore are executed more automatically than others. Because these regions are executed more automatically than others, they are less prone to significant variations depending on the actual writing conditions, and therefore should represent better than other regions the distinctive features of signatures. To support our conjecture, we report and discuss the results of experiments conducted by using an algorithm for finding those regions in the signature ink and eventually using them for automatic signature verification. Index Termsâmotor learning and execution; stability region; signature verification; I
Some observations on lognormality and motor control in handwriting
Lognormality has proven to be an effective way for handwriting modeling. It assumes that handwriting is a time superimposition of a sequence of commands issued by the central nervous system, each command producing a stroke, i.e. a movement with a lognormal velocity profile. Motor control theories, however, suggest that handwriting movements result from both central and local control, thus assuming that some movements of the sequence may not be the results of an explicit command issued by the central nervous system. In the light of those observations, we present an algorithm for segmenting handwriting movements into strokes, each of which corresponds to a command issued by the central nervous system, while disregarding those that may depend on local control. Experiments on handwriting samples show that the proposed algorithm detects the same number of strokes across multiple executions of an handwriting task by the same subject, and this set of strokes provides also a good reconstruction of the movement