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Meyer A.,Free University of Berlin | Meyer A.,University of Konstanz | Giovanni Galizia C.,University of Konstanz | Nawrot M.P.,Free University of Berlin | Nawrot M.P.,Bernstein Center for Computational Neuroscience Berlin
Journal of Neurophysiology | Year: 2013

Local computation in microcircuits is an essential feature of distributed information processing in vertebrate and invertebrate brains. The insect antennal lobe represents a spatially confined local network that processes high-dimensional and redundant peripheral input to compute an efficient odor code. Social insects can rely on a particularly rich olfactory receptor repertoire, and they exhibit complex odor-guided behaviors. This corresponds with a high anatomical complexity of their antennal lobe network. In the honeybee, a large number of glomeruli that receive sensory input are interconnected by a dense network of local interneurons (LNs). Uniglomerular projection neurons (PNs) integrate sensory and recurrent local network input into an efficient spatio-temporal odor code. To investigate the specific computational roles of LNs and PNs, we measured several features of sub- and suprathreshold singlecell responses to in vivo odor stimulation. Using a semisupervised cluster analysis, we identified a combination of five characteristic features as sufficient to separate LNs and PNs from each other, independent of the applied odor-stimuli. The two clusters differed significantly in all these five features. PNs showed a higher spontaneous subthreshold activation, assumed higher peak response rates and a more regular spiking pattern. LNs reacted considerably faster to the onset of a stimulus, and their responses were more reliable across stimulus repetitions. We discuss possible mechanisms that can explain our results, and we interpret cell-type-specific characteristics with respect to their functional relevance. © 2013 the American Physiological Society.

Rolfs M.,New York University | Rolfs M.,Aix - Marseille University | Rolfs M.,Bernstein Center for Computational Neuroscience Berlin | Rolfs M.,Humboldt University of Berlin | And 3 more authors.
Current Biology | Year: 2013

We easily recover the causal properties of visual events, enabling us to understand and predict changes in the physical world. We see a tennis racket hitting a ball and sense that it caused the ball to fly over the net; we may also have an eerie but equally compelling experience of causality if the streetlights turn on just as we slam our car's door. Both perceptual [1] and cognitive [2] processes have been proposed to explain these spontaneous inferences, but without decisive evidence one way or the other, the question remains wide open [3-8]. Here, we address this long-standing debate using visual adaptation - a powerful tool to uncover neural populations that specialize in the analysis of specific visual features [9-12]. After prolonged viewing of causal collision events called "launches" [1], subsequently viewed events were judged more often as noncausal. These negative aftereffects of exposure to collisions are spatially localized in retinotopic coordinates, the reference frame shared by the retina and visual cortex. They are not explained by adaptation to other stimulus features and reveal visual routines in retinotopic cortex that detect and adapt to cause and effect in simple collision stimuli. © 2013 Elsevier Ltd.

Neuhofer D.,Humboldt University of Berlin | Ronacher B.,Bernstein Center for Computational Neuroscience Berlin
PLoS ONE | Year: 2012

Background: Animals that communicate by sound face the problem that the signals arriving at the receiver often are degraded and masked by noise. Frequency filters in the receiver's auditory system may improve the signal-to-noise ratio (SNR) by excluding parts of the spectrum which are not occupied by the species-specific signals. This solution, however, is hardly amenable to species that produce broad band signals or have ears with broad frequency tuning. In mammals auditory filters exist that work in the temporal domain of amplitude modulations (AM). Do insects also use this type of filtering? Principal Findings: Combining behavioural and neurophysiological experiments we investigated whether AM filters may improve the recognition of masked communication signals in grasshoppers. The AM pattern of the sound, its envelope, is crucial for signal recognition in these animals. We degraded the species-specific song by adding random fluctuations to its envelope. Six noise bands were used that differed in their overlap with the spectral content of the song envelope. If AM filters contribute to reduced masking, signal recognition should depend on the degree of overlap between the song envelope spectrum and the noise spectra. Contrary to this prediction, the resistance against signal degradation was the same for five of six masker bands. Most remarkably, the band with the strongest frequency overlap to the natural song envelope (0-100 Hz) impaired acceptance of degraded signals the least. To assess the noise filter capacities of single auditory neurons, the changes of spike trains as a function of the masking level were assessed. Increasing levels of signal degradation in different frequency bands led to similar changes in the spike trains in most neurones. Conclusions: There is no indication that auditory neurones of grasshoppers are specialized to improve the SNR with respect to the pattern of amplitude modulations. © 2012 Neuhofer and Ronacher.

Sonnenschein B.,Humboldt University of Berlin | Sonnenschein B.,Bernstein Center for Computational Neuroscience Berlin | Schimansky-Geier L.,Humboldt University of Berlin
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2012

We study networks of noisy phase oscillators whose nodes are characterized by random degrees counting the number of their connections. Both these degrees and the natural frequencies of the oscillators are distributed according to a given probability density. Replacing the randomly connected network by an all-to-all coupled network with weighted edges allows us to formulate the dynamics of a single oscillator coupled to the mean field and to derive the corresponding Fokker-Planck equation. From the latter we calculate the critical coupling strength for the onset of synchronization as a function of the noise intensity, the frequency distribution, and the first two moments of the degree distribution. Our approach is applied to a dense small-world network model, for which we calculate the degree distribution. Numerical simulations prove the validity of the replacement. We also test the applicability to more sparsely connected networks and formulate homogeneity and absence of correlations in the degree distribution as limiting factors of our approach. © 2012 American Physical Society.

Kintscher M.,Charite - Medical University of Berlin | Wozny C.,Charite - Medical University of Berlin | Johenning F.W.,Charite - Medical University of Berlin | Schmitz D.,Charite - Medical University of Berlin | And 2 more authors.
Nature Communications | Year: 2013

The presynaptic terminals of synaptic connections are composed of a complex network of interacting proteins that collectively ensure proper synaptic transmission and plasticity characteristics. The key components of this network are the members of the RIM protein family. Here we show that RIM1α can influence short-term plasticity at cerebellar parallel-fibre synapses. We demonstrate that the loss of a single RIM isoform, RIM1α, leads to reduced calcium influx in cerebellar granule cell terminals, decreased release probability and consequently an enhanced short-term facilitation. In contrast, we find that presynaptic long-term plasticity is fully intact in the absence of RIM1α, arguing against its necessary role in the expression of this important process. Our data argue for a universal role of RIM1α in setting release probability via interaction with voltage-dependent calcium channels at different connections instead of synapse-specific functions. © 2013 Macmillan Publishers Limited.

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