Biomedical subjects

Nicolas Schweighofer

Publications and source records attributed to Nicolas Schweighofer.

5 recordsLinked to original sources

PubMed2006

Kinematic features of movement tunes perception and action coupling.

How do we extrapolate the final position of hand trajectory that suddenly vanishes behind a wall? Studies showing maintenance of cortical activity after objects in motion disappear suggest that internal model of action may be recalled to reconstruct the missing part of the trajectory. Although supported by neurophysiological and brain imaging studies, behavioural evidence for this hypothesis is sparse. Further, in humans, it is unknown if the recall of internal model of action at motion observation can be tuned with kinematic features of movement. Here, we propose a novel experiment to address this question. Each stimulus consisted of a dot moving either upwards or downwards, and corresponding to vertical arm movements that were masked in the last part of the trajectory. The stimulus could either move according to biological and or non-biological kinematic laws of pointing tasks. We compared subjects' estimations of the stimulus vanishing or final positions after biological and after non-biological motion displays. Subjects systematically overestimated the vanishing and final position for the two directions (up and down) and the two kinematics displayed (biological and non-biological). However, estimation of the final position decreased in precision and increased in variability for movements that violated the kinematic laws of arm pointing task. The results suggest that motion inference does not rely only upon visual extrapolating mechanisms based on past visual trajectory information. We propose that motion estimation relies on internal models that contain specific kinematic details of vertical arm movement, which can be rapidly recalled during motion observation.

Adult↗

PubMed2005

Computational motor control in humans and robots.

Computational models can provide useful guidance in the design of behavioral and neurophysiological experiments and in the interpretation of complex, high dimensional biological data. Because many problems faced by the primate brain in the control of movement have parallels in robotic motor control, models and algorithms from robotics research provide useful inspiration, baseline performance, and sometimes direct analogs for neuroscience.

Algorithms↗

PubMed2004

Chaos may enhance information transmission in the inferior olive.

Despite unique well characterized neuronal properties, such as extensive electrical coupling and low firing rates, the role of the inferior olive (IO), which is the source of the climbing fiber inputs to cerebellar Purkinje cells, is still controversial. We propose that the IO stochastically recodes the high-frequency information carried by its synaptic inputs into stochastic, low-rate spikes in its climbing fiber output. Computer simulations of realistic IO networks showed that moderate electrical coupling produced chaotic firing, which maximized the input-output mutual information. This "chaotic resonance" may allow rich error signals to reach individual Purkinje cells, even at low firing rates, allowing efficient cerebellar learning.

Animals↗

PubMed2004

Cerebellar aminergic neuromodulation: towards a functional understanding.

Although a number of neuromodulators influence the cerebellar circuitry, their functions remain largely unknown. By reviewing and combining results from data-driven and theory-driven studies, we attempt to provide an integrated systems view of cerebellar neuromodulation. First, we review the short- and long-term effects of neuromodulators on the cerebellar circuitry. Second, we review recent theories of the cerebellum and show that a number of modulatory signals are needed for powerful cerebellar learning and control. Finally, we attempt to match each theoretically derived modulatory signal with a specific neuromodulator. In particular, we propose that serotonin controls the 'responsibility' of each cerebellar unit (or microcomplex) in cerebellar learning and control; norepinephrine gates unsupervised learning in the cerebellar cortex; dopamine enhances goal-oriented cerebellar learning; and, finally, acetylcholine controls the speed of supervised learning in Purkinje cells.

Animals↗

PubMed2003

Meta-learning in reinforcement learning.

Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner. We tested our algorithm in both a simulation of a Markov decision task and in a non-linear control task. Our results show that the algorithm robustly finds appropriate meta-parameter values, and controls the meta-parameter time course, in both static and dynamic environments. We suggest that the phasic and tonic components of dopamine neuron firing can encode the signal required for meta-learning of reinforcement learning.

Algorithms↗