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Poster

Comparison of V1 receptive fields mapped with spikes and local field potentials

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83813

Biessmann,  F
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84462

Meinecke F, Bhattacharyya,  A
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84275

Veit,  J
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84096

Kretz R, Müller,  K-R
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84154

Rainer,  G
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Biessmann, F., Meinecke F, Bhattacharyya, A., Veit, J., Kretz R, Müller, K.-R., & Rainer, G. (2010). Comparison of V1 receptive fields mapped with spikes and local field potentials. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2010), Salt Lake City, UT, USA.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C148-0
Zusammenfassung
Extracellular neurophysiological recordings are typically separated in two frequency bands. Low frequency content, also called local field potentials (LFPs), reflect subthreshold integrative processes of a population of neurons. High frequency content, or multi-unit activity (MUA), contains the information conveyed by action potentials, or spikes. Spikes reflect neuronal output and are generally considered as the main currency of information in the brain. For a long time receptive field mapping methods have focused exclusively on spiking information, although some recent studies have begun to address spatial characterstics of LFP responses (Xing/Yeh/Shapley, 2009, J Neurosci). In order to compare the information about visual stimuli carried by the LFP signal and spiking activity we mapped receptive fields in primary visual cortex of the tree shrew using spike count and LFP timeseries recorded at different cortical depths. We presented white noise checkerboard patterns and sparse noise patterns and computed the standard spike triggered average (STA) receptive fields. Moreover we extracted the LFP timeseries, in different frequency bands, and the spike histograms following each stimulus and computed receptive fields for each signal employing standard canonical correlation analysis (CCA) between stimulus and LFP and spike response, respectively. Receptive fields as estimated from LFP data have two main advantages over traditional STA estimates. For one, LFP receptive fields do not suffer from binning artefacts, in contrast to STA receptive fields. Besides, CCA allows for computing a temporal filter for the respective neural signal. Receptive fields estimated using spikes were very similar to those computed from LFP signals, also for LFP bands below 20Hz. In particular the spatial extent of receptive fields computed from LFPs was comparable to that of spikes, in line with previous studies reporting a small spatial focus of LFP selectivity (Katzner et al. 2009, Neuron, Xing/Yeh/Shapley, 2009, J Neurosci). The receptive field size of both LFP and spikes varied with cortical depth. In summary our results confirm that in early stages of the visual processing hierarchy LFP signals contain to a large extent the same information about the visual stimulus as the spiking activity. In line with the above mentioned studies on non-human primates our findings suggest that the spatial selectivity of LFP signals with respect to the visual stimulus is comparable to that of spikes.