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Adaptation, Plasticity, and Coding in Sensory Systems


Experiments with behaving animals provide ample evidence for the modulation of neuronal response properties on several time-scales ranging from tens of milliseconds to minutes. Combining computational models of neuronal networks with concepts and techniques from the dynamical systems, statistical physics and machine learning fields we are interested in the mechanisms which underly changes of neural excitability, adaptation and synaptic plasticity, and their roles for computation, maintaining sensory representations and stabilizing physiological network states.

Recently we have investigated the spiking activity and synchronization properties of adaptive model neurons by applying and extending mean- field methods, a phase-reduction technique and the master stability function framework. We have characterized the effects of different types of adaptation currents -- which in real cortex are under top-down control of the brain’s neuromodulatory systems -- on spike train statistics and their potential for stabilizing spike synchrony, phase locking, and cluster states in networks. Furthermore, we have analyzed the impact of adaptation currents on spike rate oscillations in large networks, where, for example, noisy external inputs only allow for "sparse synchrony".

Another important topic in this context is the role of noise in sensory processing, and whether noise has to be considered beneficial or detrimental. For that purpose we have developed new ways to quantify noise correlations. These methods are used to analyze dependency structures within populations of neurons that have long been ignored.

Acknowledgements: Research was funded by BMBF, DFG and the Technische Universität Berlin.

Selected Publications:

Augustin, M., Ladenbauer, J., Baumann, F. and Obermayer, K. (2017). Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation. PLoS Computational Biology, 13

Ladenbauer, J., Augustin, M. and Obermayer, K. (2014). How Adaptation Currents Change Threshold, Gain and Variability of Neuronal Spiking. Journal of Neurophysiology, 111, 939–953.

Ladenbauer, J., Lehnert, J., Rankoohi, H., Dahms, T., Schöll, E. and Obermayer, K. (2013). Adaptation Controls Synchrony and Cluster States of Coupled Threshold-Model Neurons. Physical Review E, 88, 042713.

Augustin, M., Ladenbauer, J. and Obermayer, K. (2013). How Adaptation Shapes Spike Rate Oscillations in Recurrent Neuronal Networks. Front. Comput. Neurosci., 7

Ladenbauer, J., Augustin, M., Shiau, L. and Obermayer, K. (2012). Impact of Adaptation Currents on Synchronization of Coupled Exponential Integrate-and-Fire Neurons. PLoS Computational Biology, 8

Onken, A., Grünewälder, S., Munk, M. and Obermayer, K. (2009). Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation. PLoS Comput. Biol., 5, e1000577.

Wimmer, K., Hildebrandt, K., Henning, R. and Obermayer, K. (2008). Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2. PLoS Computational Biology, 4

Young, J., Waleszczyk, W., Wang, C., Calford, M., Dreher, B. and Obermayer, K. (2007). Cortical Reorganization Consistent with Spike Timing- but not Correlation-Dependent Plasticity. Nat. Neurosci., 10, 887 – 889.

Schwabe, L. and Obermayer, K. (2005). Learning Top-Down Gain Control in a Recurrent Network Model of a Visual Cortical Area. Vision Research, 45, 3202 – 3209.

Schwabe, L. and Obermayer, K. (2005). Adaptivity of Tuning Functions in a Generic Recurrent Network Model of a Cortical Hypercolumn. Journal of Neuroscience, 25, 3323 – 3332.

Beck, O., Chistiakova, M., Obermayer, K. and Volgushev, M. (2005). Adaptation of Synaptic Connections to Layer 2/3 Pyramidal Cells in Rat Visual Cortex. Journal of Neurophysiology, 94, 363 – 376.

Wenning, G., Hoch, T. and Obermayer, K. (2005). Detection of Pulses in a Colored Noise Setting. Physical Review E, 71, 21902.

Wenning, G. and Obermayer, K. (2003). Activity Driven Adaptive Stochastic Resonance. Physical Review Letters, 90, 120602.

Hoch, T., Wenning, G. and Obermayer, K. (2003). Optimal Noise-Aided Signal Transmission through Populations of Neurons. Physical Review E, 68, 11911.

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