@article{, author = {Angelidis, Emmanouil and Buchholz, Emanuel and Arreguit O'Neil, Jonathan Patrick and Roug{\'{e}}, Alexis and Stewart, Terrence and von Arnim, Axel and Knoll, Alois and Ijspeert, Auke}, title = {A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards}, journal = {ArXiv preprint}, year = {2021}, month = jan, abstract = {Central Pattern Generators (CPGs) models have been long used to investigate both the neural mechanisms that underlie animal locomotion as well as a tool for robotic research. In this work we propose a spiking CPG neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. To construct our CPG model, we employ the naturally emerging dynamical systems that arise through the use of recurrent neural populations in the Neural Engineering Framework (NEF). We define the mathematical formulation behind our model, which consists of a system of coupled abstract oscillators modulated by high-level signals, capable of producing a variety of output gaits. We show that with this mathematical formulation of the Central Pattern Generator model, the model can be turned into a Spiking Neural Network (SNN) that can be easily simulated with Nengo, an SNN simulator. The spiking CPG model is then used to produce the swimming gaits of a simulated lamprey robot model in various scenarios. We show that by modifying the input to the network, which can be provided by sensory information, the robot can be controlled dynamically in direction and pace. The proposed methodology can be generalized to other types of CPGs suitable for both engineering applications and scientific research. We test our system on two neuromorphic platforms, SpiNNaker and Loihi. Finally, we show that this category of spiking algorithms shows a promising potential to exploit the theoretical advantages of neuromorphic hardware in terms of energy efficiency and computational speed.}, keywords = {neurorobotics, neuromorphic, neuromorphic computing, HBP, NRP, virtual, robotics Neuromorphic Computing, HBP, Human Brain Project, Neurorobotics, Neurorobotics Platform, Neuroscience, Artificial Intelligence, KI, Spiking Neural Networks}, url = {https://arxiv.org/abs/2101.07001}, } @article{, author = {Allegra Mascaro, Anna Letizia and Falotico, Egidio and Petkoski, Spase and Pasquini, Maria and Vannucci, Lorenzo and Tort-Colet, Nuria and Conti, Emilia and Resta, Francesco and Spalletti, Cristina and Tata Ramalingasetty, Shravan and von Arnim, Axel and Formento, Emanuele and Angelidis, Emmanouil and Blixhavn, Camilla and Leergaard, Trygve and Caleo, Matteo and Destexhe, Alain and Ijspeert, Auke and Micera, Silvestro and Laschi, Cecilia and Jirsa, Viktor and Gewaltig, Marc-Oliver and Pavone, Francesco}, title = {Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience}, journal = {Frontiers in Systems Neuroscience}, year = {2020}, month = jul, timestamp = 2020.07.07, abstract = {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.}, doi = {https://doi.org/10.3389/fnsys.2020.00031}, keywords = {Neuromorphic Computing, HBP, Human Brain Project, Neurorobotics, Neurorobotics Platform, Neuroscience, Artificial Intelligence, KI, Spiking Neural Networks neurorobotics, neuromorphic, neuromorphic computing, HBP, NRP, virtual, robotics}, url = {https://www.frontiersin.org/articles/10.3389/fnsys.2020.00031/full}, }