Spatially organized spike correlation in cat visual cortex
Introduction
Most cells in the primary visual cortex are selective for orientation and respond best, i.e. by increasing their rates, to their optimal stimulus. Neurons located in different columns but with same stimulus preference coherently show high activity during the same, optimal stimulus, as indirectly shown by optical imaging. Moreover, it has been shown that the firing rate of a spontaneously active single neuron strongly depends on the instantaneous pattern of the ongoing population activity in a larger cortical area [10]. Very similar patterns of population activity were observed, both when the neuron fired spontaneously and when driven by its optimal stimulus [5]. On the other hand, the temporal coding hypothesis suggests that coordinated spiking activity on a fine temporal scale should occur in the nervous system [6] and is suggested to be used for information coding. Accordingly, modern theories of the primary visual pathway contain spatial and temporal aspects accounting for the observed cell behavior in order to explain visual information processing.
We therefore approach the question of how the concept of temporal coding of single neurons are related to the overall functional architecture. More specifically, we ask if correlated neurons are arranged in a specific spatial organization that may be related to maps of orientation tuning. Parallel spike recordings based on using a electrode grid (Utah electrode array, Bionic Technologies, Inc., Salt Lake City, UT, USA) covering an area of of cat visual cortex [11] allow us to address the question of the relation of correlated neuronal activity to the distance and the spatial arrangement of the recording sites. Data were recorded from area 17 of anesthetized cat during spontaneous activity, and under full-flash treatment with two different stimulus intensities. We first analyze the simultaneous multi-unit recordings for pairwise correlations using cross-correlation analysis and evaluate their significance using boot-strap techniques. In a next step, we identify groups of correlated pairs that are highly mutually intra-correlated. Finally, mapping these groups back onto the electrode positions and thereby to cortical space, allows us to relate their spatial arrangement to the spatial scales found for orientation tuning maps.
Section snippets
Detection of correlated spiking activity
We analyzed the spiking activities that were recorded from the grid of electrodes (Fig. 1) for pairwise correlations, during spontaneous activity (SP; no stimulus) and during full-flash stimulation with two different intensities. We segmented epochs of high intensity (HI) and low intensity (LI) into two separate data sets and analyzed them separately. For simplicity we restricted ourselves to the evaluation of the multi-unit activities (MUA), and requested a minimal firing rate of for
Discussion
This study investigated the spatial organization of correlated activity in cat visual cortex. Multi-unit activities were recorded by a grid spanning an area of allowing us to investigate the relation of correlated MUA pairs to spatial structure. On average 5% of all possible pairs were significantly correlated. To capture the correlation structure, we constructed a graph with each MUA being a node and assigning edges to significantly correlated MUAs. This led to the finding that the
Acknowledgments
Partial funding by the BMBF (BCCN Berlin, grant 01GQ0413), the Stifterverband für die Deutsche Wissenschaft, and the Volkswagen Foundation. This work was carried out while Sonja Grün was based at the Freie Universität in Berlin, Germany.
PD Dr. Sonja Grün was born in 1960 in Germany, where she obtained her Diploma in Physics (Eberhard-Karls University Tübingen). She did her Ph.D. work in the field of computational neuroscience at the Ruhr-University Bochum, Germany, and at the Weizmann Institute of Science, Rehovot, Israel, and obtained her Ph.D. in physics (Ruhr-University Bochum). After her post-doctoral work at the Hebrew University in Jerusalem, Israel, she worked as a senior fellow at the Max-Planck Institute for Brain
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PD Dr. Sonja Grün was born in 1960 in Germany, where she obtained her Diploma in Physics (Eberhard-Karls University Tübingen). She did her Ph.D. work in the field of computational neuroscience at the Ruhr-University Bochum, Germany, and at the Weizmann Institute of Science, Rehovot, Israel, and obtained her Ph.D. in physics (Ruhr-University Bochum). After her post-doctoral work at the Hebrew University in Jerusalem, Israel, she worked as a senior fellow at the Max-Planck Institute for Brain Research in Frankfurt/M, Germany. From 2002 to 2006 she was an Assistant Professor for Neuroinformatics/Theoretical Neuroscience at the Free University in Berlin, Germany and was a founding member of the Bernstein Center for Computational Neuroscience in Berlin. Since 9/2006 she is the head of a research unit at the RIKEN Brain Science Institute in Wako, Japan. Her main interests are in statistical neuroscience, which includes modeling of stochastic processes and the development of data analysis techniques for multiple parallel neuronal time series.
Denise Berger was born in 1980 in Germany. In 2005, she received her M.Sc. in bioinformatics from the Free University in Berlin, Germany. Currently, she is working on her Ph.D. thesis in the field of Computational Neuroscience at the Free University in Berlin, and is a member of the Bernstein Center for Computational Neuroscience in Berlin (BCCN). Her current interests are in statistical neuroscience and the analysis of massively parallel electrophysiological data.