Bernstein Center for Computational Neuroscience Gottingen

Göttingen, Germany

Bernstein Center for Computational Neuroscience Gottingen

Göttingen, Germany
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Garvert M.M.,Max Planck Institute of Neurobiology | Garvert M.M.,University College London | Gollisch T.,Max Planck Institute of Neurobiology | Gollisch T.,University of Gottingen | Gollisch T.,Bernstein Center for Computational Neuroscience Gottingen
Neuron | Year: 2013

Retinal ganglion cells react to changes in visual contrast by adjusting their sensitivity and temporal filtering characteristics. This contrast adaptation has primarily been studied under spatially homogeneous stimulation. Yet, ganglion cell receptive fields are often characterized by spatial subfields, providing a substrate for nonlinear spatial processing. This raises the question whether contrast adaptation follows a similar subfield structure or whether it occurs globally over the receptive field even for local stimulation. We therefore recorded ganglion cell activity in isolated salamander retinas while locally changing visual contrast. Ganglion cells showed primarily global adaptation characteristics, with notable exceptions in certain aspects of temporal filtering. Surprisingly, some changes in filtering were most pronounced for locations where contrast did not change. This seemingly paradoxical effect can be explained by a simple computational model, which emphasizes the importance of local nonlinearities in the retina and suggests a reevaluation of previously reported local contrast adaptation. © 2013 Elsevier Inc.


Bolinger D.,Max Planck Institute of Neurobiology | Bolinger D.,Bernstein Center for Computational Neuroscience Munich | Gollisch T.,Max Planck Institute of Neurobiology | Gollisch T.,Bernstein Center for Computational Neuroscience Munich | And 2 more authors.
Neuron | Year: 2012

Neurons often integrate information from multiple parallel signaling streams. How a neuron combines these inputs largely determines its computational role in signal processing. Experimental assessment of neuronal signal integration, however, is often confounded by cell-intrinsic nonlinear processes that arise after signal integration has taken place. To overcome this problem and determine how ganglion cells in the salamander retina integrate visual contrast over space, we used automated online analysis of recorded spike trains and closed-loop control ofthe visual stimuli to identify different stimulus patterns that give the same neuronal response. These iso-response stimuli revealed a threshold-quadratic transformation as a fundamental nonlinearity within the receptive field center. Moreover, fora subset of ganglion cells, the method revealed an additional dynamic nonlinearity that renders thesecells particularly sensitive to spatially homogeneous stimuli. This function is shown to arise from a local inhibition-mediated dynamic gain control mechanism. Understanding mechanisms of neuronal signal integration is complicated by cell-intrinsic nonlinear processes arising after integration has taken place. Bölinger and Gollisch assess how nonlinearities affect signal integration in retinal ganglion cells (RGCs), revealing mechanisms that provide some RGCs with particular sensitivity to spatially homogeneous stimulation. © 2012 Elsevier Inc.


Uhlig M.,Bernstein Center for Computational Neuroscience Gottingen | Levina A.,Bernstein Center for Computational Neuroscience Gottingen | Geisel T.,Bernstein Center for Computational Neuroscience Gottingen | Herrmann J.M.,Bernstein Center for Computational Neuroscience Gottingen | Herrmann J.M.,University of Edinburgh
Frontiers in Computational Neuroscience | Year: 2013

Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network endowed with Hebbian learning only does not allow for simultaneous information storage and criticality. However, the critical regime is can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude critical-ity if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity. © 2013 Uhlig, Levina, Geisel and Herrmann.


Kuhn N.K.,University of Gottingen | Kuhn N.K.,Bernstein Center for Computational Neuroscience Gottingen | Gollisch T.,University of Gottingen | Gollisch T.,Bernstein Center for Computational Neuroscience Gottingen
Journal of Neuroscience | Year: 2016

The processing of motion in visual scenes is important for detecting and tracking moving objects as well as for monitoring self-motion through the induced optic flow. Specialized neural circuits have been identified in the vertebrate retina for detecting motion direction or for distinguishing between object motion and self-motion, although little is known about how information about these distinct features of visual motion is combined. The salamander retina, which is a widely used model system for analyzing retinal function, contains object-motion-sensitive (OMS) ganglion cells, which strongly respond to local motion signals but are suppressed by global image motion. Yet, direction-selective (DS) ganglion cells have been conspicuously absent from characterizations of the salamander retina, despite their ubiquity in other model systems. We here show that the retina of axolotl salamanders contains at least two distinct classes of DS ganglion cells. For one of these classes, the cells display a strong preference for local over global motion in addition to their direction selectivity (OMS-DS cells) and thereby combine sensitivity to two distinct motion features. The OMS-DS cells are further distinct from standard (non-OMS) DS cells by their smaller receptive fields and different organization of preferred motion directions. Our results suggest that the two classes of DS cells specialize to encode motion direction of local and global motion stimuli, respectively, even for complex composite motion scenes. Furthermore, although the salamander DS cells are OFF-type, there is a strong analogy to the systems of ON and ON-OFF DS cells in the mammalian retina. © 2016 the authors.


Takeshita D.,University of Gottingen | Takeshita D.,Bernstein Center for Computational Neuroscience Gottingen | Gollisch T.,University of Gottingen | Gollisch T.,Bernstein Center for Computational Neuroscience Gottingen
Journal of Neuroscience | Year: 2014

Throughout different sensory systems, individual neurons integrate incoming signals over their receptive fields. The characteristics of this signal integration are crucial determinants for the neurons' functions. For ganglion cells in the vertebrate retina, receptive fields are characterized by the well-known center-surround structure and, although several studies have addressed spatial integration in the receptive field center, little is known about how visual signals are integrated in the surround. Therefore, we set out here to characterize signal integration and to identify relevant nonlinearities in the receptive field surround of ganglion cells in the isolated salamander retina by recording spiking activity with extracellular electrodes under visual stimulation of the center and surround. To quantify nonlinearities of spatial integration independently of subsequent nonlinearities of spike generation, we applied the technique of iso-response measurements as follows: Using closed-loop experiments, we searched for different stimulus patterns in the surround that all reduced the center-evoked spiking activity by the same amount. The identified iso-response stimuli revealed strongly nonlinear spatial integration in the receptive field surrounds of all recorded cells. Furthermore, cell types that had been shown previously to have different nonlinearities in receptive field centers showed similar surround nonlinearities but differed systematically in the adaptive characteristics of the surround. Finally, we found that there is an optimal spatial scale of surround suppression; suppression was most effective when surround stimulation was organized into subregions of several hundred micrometers in diameter, indicating that the surround is composed of subunits that have strong center-surround organization themselves. © 2014 the authors.


Westendorff S.,Bernstein Center for Computational Neuroscience Gottingen | Gail A.,Bernstein Center for Computational Neuroscience Gottingen
Experimental Brain Research | Year: 2011

Reach movement planning involves the representation of spatial target information in different reference frames. Neurons at parietal and premotor stages of the cortical sensorimotor system represent target information in eye-or hand-centered reference frames, respectively. How the different neuronal representations affect behavioral parameters of motor planning and control, i.e. which stage of neural representation is relevant for which aspect of behavior, is not obvious from the physiology. Here, we test with a behavioral experiment if different kinematic movement parameters are affected to a different degree by either an eye-or hand-reference frame. We used a generalized anti-reach task to test the influence of stimulus-response compatibility (SRC) in eye-and hand-reference frames on reach reaction times, movement times, and endpoint variability. While in a standard anti-reach task, the SRC is identical in the eye-and hand-reference frames, we could separate SRC for the two reference frames. We found that reaction times were influenced by the SRC in eye-and hand-reference frame. In contrast, movement times were only influenced by the SRC in hand-reference frame, and endpoint variability was only influenced by the SRC in eye-reference frame. Since movement time and endpoint variability are the result of planning and control processes, while reaction times are consequences of only the planning process, we suggest that SRC effects on reaction times are highly suited to investigate reference frames of movement planning, and that eye-and hand-reference frames have distinct effects on different phases of motor action and different kinematic movement parameters. © 2010 The Author(s).


Grabow C.,Max Planck Institute for Dynamics and Self-Organization | Grosskinsky S.,University of Warwick | Timme M.,Max Planck Institute for Dynamics and Self-Organization | Timme M.,Bernstein Center for Computational Neuroscience Gottingen
Physical Review Letters | Year: 2012

Collective dynamics on small-world networks emerge in a broad range of systems with their spectra characterizing fundamental asymptotic features. Here we derive analytic mean-field predictions for the spectra of small-world models that systematically interpolate between regular and random topologies by varying their randomness. These theoretical predictions agree well with the actual spectra (obtained by numerical diagonalization) for undirected and directed networks and from fully regular to strongly random topologies. These results may provide analytical insights to empirically found features of dynamics on small-world networks from various research fields, including biology, physics, engineering, and social science. © 2012 American Physical Society.


Nagler J.,Max Planck Institute for Dynamics and Self-Organization | Nagler J.,University of Gottingen | Levina A.,Max Planck Institute for Dynamics and Self-Organization | Levina A.,Bernstein Center for Computational Neuroscience Gottingen | And 3 more authors.
Nature Physics | Year: 2011

How a complex network is connected crucially impacts its dynamics and function. Percolation, the transition to extensive connectedness on gradual addition of links, was long believed to be continuous, but recent numerical evidence of 'explosive percolationg' suggests that it might also be discontinuous if links compete for addition. Here we analyse the microscopic mechanisms underlying discontinuous percolation processes and reveal a strong impact of single-link additions. We show that in generic competitive percolation processes, including those showing explosive percolation, single links do not induce a discontinuous gap in the largest cluster size in the thermodynamic limit. Nevertheless, our results highlight that for large finite systems single links may still induce substantial gaps, because gap sizes scale weakly algebraically with system size. Several essentially macroscopic clusters coexist immediately before the transition, announcing discontinuous percolation. These results explain how single links may drastically change macroscopic connectivity in networks where links add competitively. © 2011 Macmillan Publishers Limited. All rights reserved.


Timme M.,Max Planck Institute for Dynamics and Self-Organization | Timme M.,Bernstein Center for Computational Neuroscience Gottingen | Timme M.,University of Gottingen | Casadiego J.,Max Planck Institute for Dynamics and Self-Organization
Journal of Physics A: Mathematical and Theoretical | Year: 2014

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity. © 2014 IOP Publishing Ltd.


Kriener B.,Norwegian University of Life Sciences | Kriener B.,Max Planck Institute for Dynamics and Self-Organization | Kriener B.,Bernstein Center for Computational Neuroscience Gottingen
Chaos | Year: 2012

Under which conditions can a network of pulse-coupled oscillators sustain stable collective activity states? Previously, it was shown that stability of the simplest pattern conceivable, i.e., global synchrony, in networks of symmetrically pulse-coupled oscillators can be decided in a rigorous mathematical fashion, if interactions either all advance or all retard oscillation phases ("mono-interaction network"). Yet, many real-world networks-for example neuronal circuits-are asymmetric and moreover crucially feature both types of interactions. Here, we study complex networks of excitatory (phase-advancing) and inhibitory (phase-retarding) leaky integrate-and-fire (LIF) oscillators. We show that for small coupling strength, previous results for mono-interaction networks also apply here: pulse time perturbations eventually decay if they are smaller than a transmission delay and if all eigenvalues of the linear stability operator have absolute value smaller or equal to one. In this case, the level of inhibition must typically be significantly stronger than that of excitation to ensure local stability of synchrony. For stronger coupling, however, network synchrony eventually becomes unstable to any finite perturbation, even if inhibition is strong and all eigenvalues of the stability operator are at most unity. This new type of instability occurs when any oscillator, inspite of receiving inhibitory input from the network on average, can by chance receive sufficient excitatory input to fire a pulse before all other pulses in the system are delivered, thus breaking the near-synchronous perturbation pattern. © 2012 American Institute of Physics.

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