Home Research Project Details B3 - Theory of Spike-driven Plasticity in Circuits with Nonlinear Coupling
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B3 - Theory of Spike-driven Plasticity in Circuits with Nonlinear Coupling

Marc Timme

Patterns of precisely timed and spatially distributed spikes have been experimentally observed in different neuronal systems; they correlate with external stimuli and internal events and are thus considered key features of neural computation [Abeles 1991, Gruen et al. 1997, Hahnloser et al. 2002]. One possible explanation for their occurrence is the existence of excitatorily coupled feed-forward structures, synfire-chains that are presumed to be embedded in large recurrent neuronal circuits. Key original studies, e.g. [Abeles 1991, Diesmann et al. 1999], determined under which conditions on neural and coupling parameters such chains are capable of stably propagating synchronous activity even if they receive additional external noise modelling recurrent inputs. However, actually embedding such chains in recurrent circuit models turned out to be a major problem because, due to feedback mechanisms, neural activity tended to either become epileptic “synfire-explosion”) or die out rapidly. Only in 2008, Kumar et al. [2008] explicitly included conductance-based synapses and succeeded to embed dense feed-forward chains that stably propagate synchronous activity.

The conceptual question, how synapses may self-organize and stabilize to enable spike pattern, however, remains unsolved.

Moreover, the existence of densely coupled feed-forward anatomy in cortical circuits might be questionable. Since a few years the complementary concept has substantially progressed that certain spike patterns and coordinated, synchronized spiking activity may arise as attractors of microscopic recurrent circuits [Jin 2002, Vogels et al. 2005, Memmesheimer et al. 2006, Gong et al. 2007, Jahnke et al. 2008, Taramae et al. 2008, Kirst et al. 2009].

Most of these works still rely on the theoretical simplification assuming that synapses were static and it is still unclear how observed patterns of coordinated spikes on a microscopic (neuron- and spike-resolved) level could be learned or self-organize in the cooperative, high-dimensional dynamics of large circuits. In the proposed project we will study under which conditions and through which mechanisms learning and self-organization of spatio-temporal spike patterns becomes possible in plastic recurrent networks. The nonlinear dendritic coupling uncovered recently [Polsky et al. 2004] in single neuron experiments strongly enhances simultaneous excitatory inputs and thus actively supports synchrony propagation and self-organization of spatio-temporally coordinated spiking activity. At the same time, recent theoretical progress (Kielblock et al, unpublished) highlight that spike-resolved plasticity mechanisms (in particular short term plasticity and long term, spike-time dependent plasticity [Morrison et al. 2007, Leibold et al. 2008]) may well be investigated theoretically in microscopic neural circuit models.

We will thus study how (quantitatively) nonlinear dendritic enhancement may provide a useful trade-off against dense feed-forward anatomy in supporting synchrony propagation, both in synfire chains and in recurrent, purely random circuits and how propagating dynamic “chains” may emerge in much more sparsely connected (possibly non-feed-forward) circuits by spike-time dependent plasticity [Morrison et al. 2007, Leibold et al. 2008] in a self-organized way.


Belongs to Group(s):
Network Dynamics

Is part of  Section B 

Members working within this Project:
Jahnke, Sven 
Timme, Marc 
Arnoldt, Hinrich  

Selected Publication(s):

Breuer, D, Timme, M, and Memmesheimer, RM (2014).
Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling
Physical Review X 4((1) 011053):1-23.

Jahnke, S, Memmesheimer, RM, and Timme, M (2014).
Hub-activated signal transmission in complex networks
Physical Review E 89((3) 030701):1-5.

Jahnke, S, Memmesheimer, RM, and Timme, M (2014).
Oscillation-Induced Signal Transmission and Gating in Neural Circuits
Plos Computational Biology 10(12):e1003940.

Timme, M, and Casadiego, J (2014).
Revealing networks from dynamics: an introduction
Journal of Physics a-Mathematical and Theoretical 47(34):343001 1-36.

Bick, C, Kolodziejski, C, and Timme, M (2013).
Stalling chaos control accelerates convergence
New Journal of Physics 15(063038):063038 (10pp).

Jahnke, S, Memmesheimer, R, and Timme, M (2013).
Propagating synchrony in feed-forward networks
Frontiers in Computational Neuroscience 7(Article 153 ):1-25.

Tetzlaff, C, Kolodziejski, C, Timme, M, Tsodyks, M, and Wörgötter, F (2013).
Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation
Plos Computational Biology 9(10):1-12.

Grabow, C, Grosskinsky, S, and Timme, M (2012).
Small-World Network Spectra in Mean-Field Theory
Physical Review Letters 108(21):218701.

Memmesheimer, RM, and Timme, M (2012).
Non-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks
Plos Computational Biology 8(4):e1002384.

Tetzlaff, C, Kolodziejski, C, Timme, M, and Wörgötter, FA (2012).
Analysis of synaptic scaling in combination with Hebbian plasticity in several simple networks
Frontiers in Computational Neuroscience 6(36):1-17.

Gorur Shandilya, S, and Timme, M (2011).
Inferring network topology from complex dynamics
New J. Phys. 13:013004.

Grabow, C, Grosskinsky, S, and Timme, M (2011).
Speed of Complex Network Synchronization
Eur. Phys. J.B. 84:613.

Kielblock, H, Kirst, C, and Timme, M (2011).
Breakdown of order preservation in symmetric oscillator networks with pulse-coupling
Chaos 21:025113.

Nagler, J, Levina, A, and Timme, M (2011).
Impact of single links in competitive percolation
Nature Physics 7:265-270.

Tetzlaff, C, Kolodziejski, C, Timme, M, and Wörgötter, F (2011).
Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity
Frontiers Comput. Neurosci. 5:47.

van Bussel, F, Kriener, B, and Timme, M (2011).
Inferring synaptic connectivity from spatio-temporal spike patterns
Frontiers Comput. Neurosci. 5(3):1-9.