Jun 24, 2019
In conversation with our two new group leaders
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Neural dynamics of learning and computation
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.
Zenke, F. and Ganguli, S. (2018) SuperSpike: Supervised learning in multi-layer spiking neural networksNeural Comput 30, 1514-1541. doi: 10.1162/neco_a_01086
Gjoni, E., Zenke, F., Bouhours, B., and Schneggenburger R. (2018) Specific synaptic input strengths determine the computational properties of excitation-inhibition integration in a sound localization circuitJ Physiol. doi: 10.1113/JP276012
Zenke, F.*, Poole, B.*, and Ganguli, S. (2017) Continual Learning Through Synaptic IntelligenceProceedings of the 34th International Conference on Machine Learning (ICML), pp. 3987-3995
* Co-first author
Zenke, F., Gerstner, W., Ganguli, S. (2017) The temporal paradox of Hebbian learning and homeostatic plasticityCurr Opin Neurobiol 43, 166-176
Zenke, F. and Gerstner, W. (2017) Hebbian plasticity requires compensatory processes on multiple timescalesPhil Trans R Soc B 372, 20160259. doi: 10.1098/rstb.2016.0259
Zenke, F., Agnes, E. J., Gerstner, W. (2015) Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networksNature Commun 6. doi: 10.1038/ncomms7922
Ziegler, L., Zenke, F., Kastner, D.B., Gerstner, W. (2015) Synaptic Consolidation: From Synapses to Behavioral ModelingJ Neurosci 35, 1319-1334. doi: 10.1523/JNEUROSCI.3989-14.2015
Zenke, F. and Gerstner, W. (2014) Limits to high-speed simulations of spiking neural networks using general-purpose computersFront Neuroinform 8, 76. doi: 10.3389/fninf.2014.00076
Zenke, F., Hennequin, G., Gerstner, W. (2013) Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate DetectorPLoS Comput Biol 9, e1003330. doi: 10.1371/journal.pcbi.1003330
Lütcke, H., Gerhard, F., Zenke, F., Gerstner, W., Helmchen, F. (2013) Inference of neuronal network spike dynamics and topology from calcium imaging dataFront Neural Circuits 7. doi: 10.3389/fncir.2013.00201
Vogels, T.P.*, Sprekeler, H.*, Zenke, F., Clopath, C., Gerstner, W. (2011) Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory NetworksScience 334, 1569-1573. doi: 10.1126/science.1211095
* Co-first author
Full list of publications
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