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Classification of cortical microcircuits based on micro-electrode-array data from slices of rat barrel cortex.

Authors :
Bakker, R.
Schubert, D.
Levels, K.
Bezgin, G.
Bojak, I.
Kotter, R.
Bakker, R.
Schubert, D.
Levels, K.
Bezgin, G.
Bojak, I.
Kotter, R.
Source :
Neural Networks; 1159; 1168; 0893-6080; 8; 22; ~Neural Networks~1159~1168~~~0893-6080~8~22~~
Publication Year :
2009

Abstract

Contains fulltext : 75307.pdf (publisher's version ) (Closed access)<br />The bewildering complexity of cortical microcircuits at the single cell level gives rise to surprisingly robust emergent activity patterns at the level of laminar and columnar local field potentials (LFPs) in response to targeted local stimuli. Here we report the results of our multivariate data-analytic approach based on simultaneous multi-site recordings using micro-electrode-array chips for investigation of the microcircuitry of rat somatosensory (barrel) cortex. We find high repeatability of stimulus-induced responses, and typical spatial distributions of LFP responses to stimuli in supragranular, granular, and infragranular layers, where the last form a particularly distinct class. Population spikes appear to travel with about 33 cm/s from granular to infragranular layers. Responses within barrel related columns have different profiles than those in neighbouring columns to the left or interchangeably to the right. Variations between slices occur, but can be minimized by strictly obeying controlled experimental protocols. Cluster analysis on normalized recordings indicates specific spatial distributions of time series reflecting the location of sources and sinks independent of the stimulus layer. Although the precise correspondences between single cell activity and LFPs are still far from clear, a sophisticated neuroinformatics approach in combination with multi-site LFP recordings in the standardized slice preparation is suitable for comparing normal conditions to genetically or pharmacologically altered situations based on real cortical microcircuitry.

Details

Database :
OAIster
Journal :
Neural Networks; 1159; 1168; 0893-6080; 8; 22; ~Neural Networks~1159~1168~~~0893-6080~8~22~~
Publication Type :
Electronic Resource
Accession number :
edsoai.on1284161067
Document Type :
Electronic Resource