Computational Neuroscience and Neurotheory
The Vogels Group is looking to build models of neurons and neuronal networks that distill and re-articulate the current knowledge of how nervous systems compute at a mechanistic level. In particular, the group is interested in the neuronal interplay of excitatory and inhibitory activity in cortex and how these dynamics can form reliable sensory perceptions, stable memories, and motor outputs.
More specifically, the work in our lab is divided into three main areas:
The group aims to find the rules governing how the brain updates its synaptic connections in order to learn and adapt to a changing world. In collaboration with experimentalists working on various systems from humans to the fruit fly, we build mechanistic models of synaptic plasticity to help elucidate (i) how plasticity differs across different cell types in cortical networks, (ii) how learning is guided by neuromodulatory signals, (iii) how learning changes across development, and (iv) how changes in synaptic connections affect the resulting neuronal network dynamics used for computation.
2) Network dynamics and computation
The Vogels group seeks to understand how neuronal networks process and transform sensory inputs, store and manipulate memories, and send motor outputs. By building and analysing models of spiking and firing-rate neuronal networks, the group studies the role of inhibition and excitatory-inhibitory balance in processing and gating the flow of information, and how contextual and reinforcement signals modify network properties to produce the flexible and complex dynamics seen in current large-scale neuronal recordings.
3) Ion channels and single-neuron biophysics
The Vogels Group builds detailed biophysical models of single neurons in order to understand the complex input-output relationships at the level of single neurons and their dendritic branches. In collaboration with researchers at EPFL (Lausanne) and the CNCB (Oxford), they have created an extensive database of ion channel models and their relationships, to facilitate better experimentally-constrained modelling (ICGenealogy). The Group is now working to expand this resource into other areas of neuroinformatics in order to help make sense of the large amounts of data that experimental and computational neuroscience currently produces.
On this site:
Predoctoral Visiting Scientist
Juan Sebastián Calderón García
Douglas Feitosa Tomé
Van Der Plas TL, Vogels TP, Manohar SG. 2022. Predictive learning enables neural networks to learn complex working memory tasks. Proceedings of Machine Learning Research. vol. 199, 518–531. View
Jia DW, Vogels TP, Costa RP. 2022. Developmental depression-to-facilitation shift controls excitation-inhibition balance. Communications biology. 5, 873. View
Christodoulou G, Vogels TP, Agnes EJ. 2022. Regimes and mechanisms of transient amplification in abstract and biological neural networks. PLoS Computational Biology. 18(8), e1010365. View
Kaneko K, Currin C, Goff KM, Wengert ER, Somarowthu A, Vogels TP, Goldberg EM. 2022. Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome. Cell Reports. 38(13), 110580. View
Confavreux BJ, Vogels TP. 2022. A familiar thought: Machines that replace us? Neuron. 110(3), 361–362. View
ReX-Link: Tim Vogels
Since 2020 Professor, Institute of Science and Technology Austria (ISTA)
2016 – 2020 Associate Professor, University of Oxford
2013 – 2018 Sir Henry Dale Wellcome Trust & Royal Society Research Fellow, University of Oxford
2014 – 2018 Kavli-FENS Scholar, European Network of Excellence in Neuroscience
2014 – 2017 Hayward Junior Research Fellow, Oriel College, University of Oxford
2010 – 2013 Marie Curie Postdoctoral Fellow, Gerstner Lab, École Polytechnique Fédérale de Lausanne
2007 – 2010 Patterson Trust Postdoctoral Fellow, Yuste Lab Columbia University of New York City
2007 PhD, Neuroscience, Abbott Lab, Brandeis University, USA
2001 ‘Vordiplom’, Physics, Technische Universität Berlin, Germany
2014 Kavli FENS Fellowship
2012 Bernstein Award for Computational Neuroscience
2003 Pulin Sampat Teaching Award
2001 Fulbright Scholarship