The development of theoretical algorithms can play a key role in many areas of neuroscience research, including modelling of biological processes, analysis of brain activity patterns, and input into brain-derived computation.

Our team of theoreticians in SP4 aims to create bridges between low-level and large-scale descriptions of the brain. We investigate models ranging from the level of single neurons (how they integrate synaptic inputs, the role of dendrites, etc.), to neuronal populations (neural networks, how they represent information), to entire brain areas (functional imaging) and cognitive processes (such as sensory processing or spatial navigation).

In the Ramp-Up Phase, we generated several algorithms for plasticity and learning, simplified models of dendrites that are compatible with neuromorphic hardware, and models of brain signals (such as the local field potential). We also investigated scenarios of learning in the presence of on-going activity in neuronal networks, models for creating realistic activity states, population-level models, and models including glial cells. Principles of brain-like computations in brain-like circuits that can be implemented on neuromorphic hardware were also a major focus. At the systems level, we built models of spatial navigation that may be useful for robotics, and models to deduce the effective connectivity from imaging experiments. In addition, we set up and run the European Institute for Theoretical Neuroscience (EITN), to foster theoretical neuroscience activities related to the HBP and to create strong interactions with the theoretical neuroscience community to bring new ideas and theories to the project.

During SGA1, we aim to develop models of the brain from cellular to network levels, including detailed, simplified, and population models. One of our first efforts will be to bridge scales, by investigating how scales interact in specific and general terms. Specifically, we will gain insight on how processes on a microscopic level express themselves parametrically on higher levels of organisation, such as neuronal populations or even entire brain areas. The development of mean-field models will enable us to directly integrate mesoscopic and macroscopic signals (e.g. LFP, EEG, up to fMRI) and therefore develop models of the brain at such scales. For instance, the plan is to reach a whole-brain model of the mouse using such population models.

Providing building blocks for other, large-scale models is a second effort. These building blocks include different brain signals, simplified models with dendrites, generic (single-compartment) models of different brain areas, algorithms for synaptic plasticity, and memory. These will allow us to investigate mechanisms for learning, memory, attention and goal-orientated behaviour, and begin to determine the way in which function emerges from structure.

Work at a more functional level will investigate fundamental aspects of brain function, such as the genesis of spontaneous activity, low-level vision, motor control, sensorimotor coordination and spatial navigation. Such models and mechanisms will be conceived so that they can be implemented directly on neuromorphic hardware.

SP Leader: Alain DESTEXHE

Deputy SP Leaders: Idan SEGEVViktor JIRSA

Work Package Leaders:


Publication highlights:

Deco, G., Ponce-Alvarez, A., Hagmann, P., Romani, G.L., Mantini, D., & Corbetta, M. (2014). How local excitation-inhibition ratio impacts the whole brain dynamics. Journal of Neuroscience, 34: 7886-7898.

Deco, G., & Kringelbach, M.L. (2014). Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron, 84: 892-905.

Dehghani, N., Peyrache, A., Telenczuk, B., Le Van Quyen, M., Halgren, E., Cash, S.S., Hatsopoulos, N.G., & Destexhe, A. (2016). Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex. Scientific Reports, 6: 23176.

Gilson, M., Moreno-Bote, R., Ponce-Alvarez, A., Ritter, P. & Deco, G. (2016). Estimation of directed effective connectivity from fMRI functional connectivity hints at asymmetries of cortical connectome. PLoS Computational Biology, 12: e1004762.

Kappel, D., Habenschuss, S., Legenstein, R., & Maass, W. (2015). Synaptic sampling: a Bayesian approach to neural network plasticity and rewiring. Advances in Neural Information Processing Systems 28 (NIPS 2015).

Kappel, D., Habenschuss, S., Legenstein, R., & Maass, W. (2015). Network plasticity as Bayesian inference. PLoS Computational Biology, 11: e1004485.

Ness, T.V.,  Remme, M.W., & Einevoll, G.T. (2016). Active subthreshold dentritic conductances shape the local field potential. Journal of Physiology, 594: 3809-3825.

Telenczuk, B., Dehghani, N., Le Van Quyen, M., Cash, S., Halgren, E.,Hatsopoulos, N.G. and Destexhe, A. Excitatory and inhibitorysingle-unit contributions to local field potentials in human andmonkey cortex. Scientific Reports: in press — preprint.

Vandoorne, K., Mechet, P., Van Vaerenbergh, T., Fiers, M., Morthier, G., Verstraeten, D., Schrauwen, B., Dambre, J., & Bienstman, P. (2014). Experimental Demonstration of Reservoir Computing on a Silicon Photonics Chip. Nature Communications, 5: 3541.

Zenke, F., Agnes, E.J., & Gerstner, W. (2015). Diverse Synaptic Plasticity Mechanisms Orchestrated to Form and Retrieve Memories in Spiking Neural Networks. Nature Communications, 6: 6922.