Arvind Kumar
Biography - Arvind Kumar
KTH Royal Institute of Technology, Stockholm, Sweden & USIAS Fellow, Institute of Cellular and Integrative Neuroscience (INCI), University of Strasbourg and CNRS, France
Arvind Kumar is an Associate Professor of Computational Neuroscience at the KTH Royal Institute of Technology (Stockholm, Sweden). He is interested in understanding dynamical properties and information processing in biological neuronal networks. In particular, his research group investigates the functional and dynamical consequences of neuronal diversity and how the interplay of network connectivity and network dynamics affects information exchange across the brain. His work has provided a powerful theoretical framework to understand communication among brain areas [see e.g. Kumar et al. 2010 Nature Reviews Neurosci. Hahn et al. 2019 Nature Reviews Neurosci.]. In addition, he is developing computational models of brain disorders and neutralizing the control system theory to develop tools to control of neural dynamics using brain stimulations methods.
Arvind Kumar studied electrical and electronics engineering and obtained a Masters in Engineering degree from the Birla Institute of Technology and Science (Pilani, India). In 2001 he attended the RIKEN Brain Science summer school and decided to switch fields to computational neuroscience. Five years later, he obtained a PhD in computational neuroscience under the supervision of Ad Aertsen and Stefan Rotter at the University of Freiburg, Germany. After that he moved to Brown University (Providence, USA) to work with Professor Mayank Mehta. He returned to his alma mater, the Bernstein Center Freiburg, as a group leader in 2009 before moving to KTH Stockholm in 2015.
Arvind Kumar spends his free time either playing cricket or analyzing cricket-related data.
During his stay in Strasbourg, Arvind Kumar will be hosted by Dr. Philippe Isope at the Institute of Cellular and Integrative Neuroscience (INCI).
Project - Functional implications of bidirectional connectivity between cerebellum and basal ganglia
01/09/2022 – 31/08/2024
Precise movements are essential for the survival of any animal. Many brain regions are involved in the performance of even the simplest of the voluntary movements. Networks in the motor cortex plan, initiate and generate activity patterns to control muscles. However, this is not sufficient. We also need to decide which actions are to be performed by carefully evaluating the value of each action. This task is performed by the basal ganglia (BG). In addition, we need the cerebellum (CB) to refine movement commands to achieve fine motor control. Given the importance of BG and CB in motor control it is no surprise that BG and/or CB dysfunctions underlie several debilitating motor disorders, such as Parkinson’s and Huntington’s diseases, ataxia, and dystonia. CB and BG are bi-directionally connected. Curiously, motor symptoms can in some cases be alleviated either by cutting CB-BG connections or by stimulating CB. However, there is little understanding of the functional role of bidirectional projections between CB and BG in both healthy and disease conditions.
In this project we will fill this gap by combining computational models, machine learning and animal experiments. As a first step we will use machine learning tools to identify various types of spiking activity patterns in the CB and BG from already available experimental data. Next and for the first time, we will simultaneously record activity of striatal neurons and Purkinje neurons in a motor skill learning task and quantify CB-BG interactions during learning and execution of motor tasks. This unique data will also be used to develop a computational model of the CB-BG network. Using computational models we will test under what condition connections between CB-BG may facilitate reinforcement learning and decision-making. Finally, we will adapt the model to mimic dystonia – a disease associated with uncontrolled movements – to study when and how stimulation of CB or BG may alleviate symptoms of the disease. Thus, the outcomes of this project will be relevant not only to neuroscientists but also to machine learning experts and clinicians.
