Aug 11, 2022
Self-Taught AI Shows Similarities to How the Brain Works
Feb 17, 2022
AI Overcomes Stumbling Block on Brain-Inspired Hardware
Dec 23, 2021
How neurons that wire together fire together
Nov 19, 2021
Eccellenza Fellowship for Friedemann Zenke
Nov 2, 2020
Building artificial neural networks inspired by the brain
|All group news|
|Computational Neuroscience Initiative Basel|
The overall aim of our research group is to understand the principles that underlie memory formation and information processing in biological neural networks. To this end, we build neural network models with experience-dependent plasticity and study how specific function can emerge through the orchestrated interplay of different plasticity mechanisms.
Mathematical models allow us to integrate and conceptualize the vast amounts of data generated by modern experiments. Moreover, models offer a unique vantage point over the high-dimensional complexity encountered in neural networks because they allow us to control factors such as noise and partial observability, which can confound experimental data. Thus, models enable us to efficiently explore new theories and help generate experimentally testable predictions.
Our modeling efforts focus on how both the structure and function of neural networks are shaped by plasticity. Specifically, we take a three-fold approach, which combines simulations, theory, and data analysis. First, to simulate large rate-coding and spiking neural networks with plasticity, we rely on high-performance computing and machine learning techniques. Second, to interpret and understand the dynamics in our models, we employ a variety of analytical tools from dynamical systems, control theory and statistical physics. Finally, to compare the high-dimensional dynamics of models with neurobiological data, we work closely with our experimental colleagues on the development and the application of practical dimensionality reduction techniques.
This is a list of selected publications from this group. For a full list of publications, please visit our Publications page and search by group name.
Cramer, B., Billaudelle, S., Kanya, S., Leibfried, A., Grübl, A., Karasenko, V., Pehle, C., Schreiber, K., Stradmann, Y., Weis, J., Schemmel, J., Zenke, F. (2022) Surrogate gradients for analog neuromorphic computing.PNAS Vol. 119 | No. 4
Cramer, B., Stradmann, Y., Schemmel, J., and Zenke, F. (2022) The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks.IEEE Transactions on Neural Networks and Learning Systems 33, 2744-2757
Wu, Y.K., and Zenke, F. (2021) Nonlinear transient amplification in recurrent neural networks with short-term plasticity.eLife 10, e71263
Zenke, F., Bohté, S.M., Clopath, C., Comsa, I.M., Göltz, J., Maass, W., Masquelier, T., Naud, R., Neftci, E.O., Petrovici, M.A., et al. (2021) Visualizing a joint future of neuroscience and neuromorphic engineering.Neuron 109, 571-575
Zenke, F., and Vogels, T.P. (2021) The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.Neural Computation 33 (4): 899-925
Zenke, F., and Neftci, E.O. (2021) Brain-Inspired Learning on Neuromorphic Substrates.Proceedings of the IEEE 1-16.
Liu, T., and Zenke, F. (2020) Finding trainable sparse networks through Neural Tangent Transfer.Proceedings of the 37th International Conference on Machine Learning (ICML)
Neftci, E.O., Mostafa, H., Zenke, F. (2019) Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks.IEEE Signal Processing Magazine 36, 51-63
Full list of publications
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