Bernstein Center for Computational Neuroscience

Berlin, Germany

Bernstein Center for Computational Neuroscience

Berlin, Germany

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Thurley K.,Ludwig Maximilians University of Munich | Thurley K.,Bernstein Center for Computational Neuroscience | Ayaz A.,University of Zürich
Current Zoology | Year: 2017

Over the last decade virtual reality (VR) setups for rodents have been developed and utilized to investigate the neural foundations of behavior. Such VR systems became very popular since they allow the use of state-of-the-art techniques to measure neural activity in behaving rodents that cannot be easily used with classical behavior setups. Here, we provide an overview of rodent VR technologies and review recent results from related research. We discuss commonalities and differences as well as merits and issues of different approaches. A special focus is given to experimental (behavioral) paradigms in use. Finally we comment on possible use cases that may further exploit the potential of VR in rodent research and hence inspire future studies. © The Author (2016).

Clemens J.,Princeton University | Kramer S.,Humboldt University of Berlin | Ronacher B.,Bernstein Center for Computational Neuroscience
Proceedings of the National Academy of Sciences of the United States of America | Year: 2014

Decision-making processes, like all traits of an organism, are shaped by evolution; they thus carry a signature of the selection pressures associated with choice behaviors. The way sexual communication signals are integrated during courtship likely reflects the costs and benefits associated with mate choice. Here, we study the evaluation of male song by females during acoustic courtship in grasshoppers. Using playback experiments and computational modeling we find that information of different valence (attractive vs. nonattractive) is weighted asymmetrically: while information associated with nonattractive features has large weight, attractive features add little to the decision to mate. Accordingly, nonattractive features effectively veto female responses. Because attractive features have so little weight, the model suggests that female responses are frequently driven by integration noise. Asymmetrical weighting of negative and positive information may reflect the fitness costs associated with mating with a nonattractive over an attractive singer, which are also highly asymmetrical. In addition, nonattractive cues tend to be more salient and therefore more reliable. Hence, information provided by them should be weighted more heavily. Our findings suggest that characterizing the integration of sensory information during a natural behavior has the potential to provide valuable insights into the selective pressures shaping decision-making during evolution.

Battaglia D.,Max Planck Institute for Dynamics and Self-Organization | Battaglia D.,Bernstein Center for Computational Neuroscience | Hansel D.,University of Paris Descartes | Hansel D.,Hebrew University of Jerusalem
PLoS Computational Biology | Year: 2011

Visually induced neuronal activity in V1 displays a marked gamma-band component which is modulated by stimulus properties. It has been argued that synchronized oscillations contribute to these gamma-band activity. However, analysis of Local Field Potentials (LFPs) across different experiments reveals considerable diversity in the degree of oscillatory behavior of this induced activity. Contrast-dependent power enhancements can indeed occur over a broad band in the gamma frequency range and spectral peaks may not arise at all. Furthermore, even when oscillations are observed, they undergo temporal decorrelation over very few cycles. This is not easily accounted for in previous network modeling of gamma oscillations. We argue here that interactions between cortical layers can be responsible for this fast decorrelation. We study a model of a V1 hypercolumn, embedding a simplified description of the multi-layered structure of the cortex. When the stimulus contrast is low, the induced activity is only weakly synchronous and the network resonates transiently without developing collective oscillations. When the contrast is high, on the other hand, the induced activity undergoes synchronous oscillations with an irregular spatiotemporal structure expressing a synchronous chaotic state. As a consequence the population activity undergoes fast temporal decorrelation, with concomitant rapid damping of the oscillations in LFPs autocorrelograms and peak broadening in LFPs power spectra. We show that the strength of the inter-layer coupling crucially affects this spatiotemporal structure. We predict that layer VI inactivation should induce global changes in the spectral properties of induced LFPs, reflecting their slower temporal decorrelation in the absence of inter-layer feedback. Finally, we argue that the mechanism underlying the emergence of synchronous chaos in our model is in fact very general. It stems from the fact that gamma oscillations induced by local delayed inhibition tend to develop chaos when coupled by sufficiently strong excitation. © 2011 Battaglia and Hansel.

Stein T.,Charite Campus Mitte | Sterzer P.,Charite Campus Mitte | Sterzer P.,Bernstein Center for Computational Neuroscience
Journal of Vision | Year: 2011

When incompatible images are presented to the two eyes, one image dominates awareness while the other is rendered invisible by interocular suppression. It has remained unclear whether complex visual information can reach high-level processing stages in the ventral visual pathway during such interocular suppression. Here, we asked whether basic face shape, which is thought to be encoded in areas of the ventral stream, can be processed without visual awareness. We measured aftereffects induced by prolonged exposure to distorted faces during continuous flash suppression. Despite constant physical stimulation, in some trials the adaptor face was fully suppressed from awareness, while in other trials it overcame suppression and became partially visible. Aftereffects were induced even by entirely invisible adaptors, albeit reduced compared to partially visible adaptors, and only when adaptor and test stimuli were presented in the same size to the same eye. However, when adaptor and test stimuli were presented to different eyes or to the same eye but in different sizes, aftereffects were restricted to partially visible adaptors. These results suggest that a monocular, low-level component of face shape adaptation escapes interocular suppression and can proceed without visual awareness. By contrast, highlevel components of basic face shape encoding involving ventral stream processing are eliminated by interocular suppression and require visual awareness. © ARVO.

Steingrube S.,Bernstein Center for Computational Neuroscience | Steingrube S.,Leibniz University of Hanover | Timme M.,Bernstein Center for Computational Neuroscience | Timme M.,Max Planck Institute for Dynamics and Self-Organization | And 5 more authors.
Nature Physics | Year: 2010

Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Present robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors by means of a simple neural control circuit, thereby generating 11 basic behavioural patterns (for example, orienting, taxis, self-protection and various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and also enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom. © 2010 Macmillan Publishers Limited. All rights reserved.

Sinz F.,University of Tübingen | Bethge M.,University of Tübingen | Bethge M.,Bernstein Center for Computational Neuroscience
PLoS Computational Biology | Year: 2013

Divisive normalization in primary visual cortex has been linked to adaptation to natural image statistics in accordance to Barlow's redundancy reduction hypothesis. Using recent advances in natural image modeling, we show that the previously studied static model of divisive normalization is rather inefficient in reducing local contrast correlations, but that a simple temporal contrast adaptation mechanism of the half-saturation constant can substantially increase its efficiency. Our findings reveal the experimentally observed temporal dynamics of divisive normalization to be critical for redundancy reduction. © 2013 Sinz, Bethge.

Shandilya S.G.,Max Planck Institute for Dynamics and Self-Organization | Shandilya S.G.,Yale University | Timme M.,Max Planck Institute for Dynamics and Self-Organization | Timme M.,Bernstein Center for Computational Neuroscience | Timme M.,University of Gottingen
New Journal of Physics | Year: 2011

Inferring the network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method for inferring the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is hardly restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology from observing a time series of state variables only. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstructing both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of external noise that distorts the original dynamics substantially. The method provides a conceptually new step towards reconstructing a variety of real-world networks, including gene and protein interaction networks and neuronal circuits. © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.

Bannert M.M.,University of Tübingen | Bannert M.M.,Bernstein Center for Computational Neuroscience | Bartels A.,University of Tübingen | Bartels A.,Bernstein Center for Computational Neuroscience
Current Biology | Year: 2013

Some everyday objects are associated with a particular color, such as bananas, which are typically yellow. Behavioral studies show that perception of these so-called color-diagnostic objects is influenced by our knowledge of their typical color, referred to as memory color [1, 2]. However, neural representations of memory colors are unknown. Here we investigated whether memory color can be decoded from visual cortex activity when color-diagnostic objects are viewed as grayscale images. We trained linear classifiers to distinguish patterns of fMRI responses to four different hues. We found that activity in V1 allowed predicting the memory color of color-diagnostic objects presented in grayscale in naive participants performing a motion task. The results imply that higher areas feed back memory-color signals to V1. When classifiers were trained on neural responses to some exemplars of color-diagnostic objects and tested on others, areas V4 and LOC also predicted memory colors. Representational similarity analysis showed that memory-color representations in V1 were correlated specifically with patterns in V4 but not LOC. Our findings suggest that prior knowledge is projected from midlevel visual regions onto primary visual cortex, consistent with predictive coding theory [3]. © 2013 Elsevier Ltd.

Hansen E.C.A.,Aix - Marseille University | Battaglia D.,Aix - Marseille University | Battaglia D.,Bernstein Center for Computational Neuroscience | Spiegler A.,Aix - Marseille University | And 2 more authors.
NeuroImage | Year: 2015

Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations. © 2014 The Authors.

Pamir E.,Bernstein Center for Computational Neuroscience
Learning & memory (Cold Spring Harbor, N.Y.) | Year: 2011

Conditioned behavior as observed during classical conditioning in a group of identically treated animals provides insights into the physiological process of learning and memory formation. However, several studies in vertebrates found a remarkable difference between the group-average behavioral performance and the behavioral characteristics of individual animals. Here, we analyzed a large number of data (1640 animals) on olfactory conditioning in the honeybee (Apis mellifera). The data acquired during absolute and differential classical conditioning differed with respect to the number of conditioning trials, the conditioned odors, the intertrial intervals, and the time of retention tests. We further investigated data in which animals were tested for spontaneous recovery from extinction. In all data sets we found that the gradually increasing group-average learning curve did not adequately represent the behavior of individual animals. Individual behavior was characterized by a rapid and stable acquisition of the conditioned response (CR), as well as by a rapid and stable cessation of the CR following unrewarded stimuli. In addition, we present and evaluate different model hypotheses on how honeybees form associations during classical conditioning by implementing a gradual learning process on the one hand and an all-or-none learning process on the other hand. In summary, our findings advise that individual behavior should be recognized as a meaningful predictor for the internal state of a honeybee--irrespective of the group-average behavioral performance.

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