Tom Tetzlaff (Institute of Mathematical Sciences and Technology (IMT), Norwegian University of Life Sciences (UMB), Ås, Norway)
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Bernstein Seminar
"Decorrelation of neuralnetwork activity by inhibitory feedback" / Wednesday, June 9, 2010, 17:15 h
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When 
Jun 09, 2010 from 05:00 PM to 07:00 PM 
Where  Lecture Hall, Hansastr. 9a 
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The Bernstein Center Freiburg Bernstein Seminar  
Tom Tetzlaff Institute of Mathematical Sciences and Technology (IMT) Norwegian University of Life Sciences (UMB) Ås, Norway Decorrelation of neuralnetwork activity by inhibitory feedback  
Wednesday, June 9, 2010 17:15 h  Lecture Hall (ground floor) BCCN building Hansastraße 9A 79104 Freiburg 
Abstract: Spatial correlations in spiketrain ensembles can seriously impair the en and decoding of information in the population rate or in the fine spatiotemporal structure of these spike trains. Recent theoretical and experimental studies showed that spike correlations in neural networks can be considerably smaller than expected based on the amount of shared presynaptic input in such systems. I will demonstrate by means of a simple linear model and simulations of networks of integrateandfire neurons that pairwise correlations and hence populationrate fluctuations in recurrent networks are actively suppressed by inhibitory feedback. To investigate the role of feedback, we calculated the power and crossspectra of the network response for the intact recurrent system and for the case where the 2ndorder statistics of the feedback signals is perturbed while the sharedinput structure and the 1storder statistics are preserved. In general, any modification of the feedback statistics causes a shift in the power and coherence of the population response. In particular, the neglect of correlations within the ensemble of feedback channels or between the external stimulus and the feedback can amplify populationrate fluctuations by orders of magnitude. This effect can be observed both in networks with purely inhibitory and in those with mixed excitatoryinhibitory coupling. In purely inhibitory networks, sharedinput correlations are canceled by negative correlations between the feedback signals. In excitatoryinhibitory networks, the responses are typically positively correlated. Here, the suppression of input correlations is not a result of the mere existence of correlations between the responses of excitatory (E) and inhibitory (I) neurons, but is instead a consequence of the heterogeneity of response correlations across different types of neuron pairs (EE, EI, II). If correlations between EE, II and EI pairs were identical, input correlations could not fall below the level imposed by the amount of shared input. I will further show that the suppression of correlations and populationrate fluctuations in recurrent networks is in general frequency (timescale) dependent. The affected frequency range is determined by the populationrate transfer properties. Spike responses of neural populations in both experiment and theory typically exhibit lowpass characteristics. Highfrequency fluctuations are therefore not affected by feedback.  
Host: Janina Kirsch  
The talk is open to the public. Guests are cordially invited! www.bcf.unifreiburg.de 