Marc-Oliver Gewaltig

Biography

Marc-Oliver Gewaltig is the Section Manager of Neurorobotics within the Simulation Neuroscience Division.

In our team we investigate the simulation-aided reconstruction of sensory-motor loops in rodents, using data-driven whole brain models and musculoskeletal body models. Of particular interest is closed-action perception loops in which an animal influences or actively controls its sensory input.

Marc-Oliver is the co-author of the Neural Simulation Tool NEST (www.nest-simulator.org), a popular tool for large-scale simulations of spiking neural networks.

Before joining Blue Brain in 2011, Marc-Oliver was Principal Scientist (1998-2011) at the Honda Research Institute Europe in Offenbach, Germany. He completed his Ph.D. in Physics in 1999.

In his spare time, Marc-Oliver enjoys skateboarding, fencing, and filming.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related units (3)

Related units

Courses taught by this person

No results

Courses taught by this person

Related research domains (10)

Related research domains

Related publications (18)

Related publications

People doing similar research (125)

People doing similar research

Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code

Marc-Oliver Gewaltig

In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.

SPRINGER2021

The aim of the neurorobotic platform of the Human Brain Project is to offer scientists and technology developers a soft - ware and hardware infrastructure that allows them to connect pre-validated brain models to detailed simulations of ro- bot bodies and environments, and to use the resulting neurorobotic systems in in-silico experiments and technology de- velopment. The first version of this platform has been released in March 2016 and is described in this contribution.

VDE Verlag2021

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience

Emmanouil Angelidis, Alain Destexhe, Egidio Falotico, Emanuele Formento, Marc-Oliver Gewaltig, Auke Ijspeert, Silvestro Micera, Maria Pasquini, Shravan Tata Ramalingasetty, Lorenzo Vannucci, Axel von Arnim

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

FRONTIERS MEDIA SA2020