Abstract
AbstractA lot of methods were created in last decade for the spatio-temporal analysis of multi-electrode array (MEA) neuronal data sets. All these methods were implemented starting from a channel to channel analysis, with a great computational effort and onerous spatial pattern recognition task. Our idea is to approach the MEA data collection from a different point of view, i.e. considering all channels simultaneously. We transform the 2D plus time MEA signal in a mono-dimensional plus time signal and elaborate it as a normal 1D signal, using the Space-Amplitude Transform method. This geometrical transformation is completely invertible and allows to employ very fast processing algorithms.
Highlights
MATERIALS AND METHODSIn last years a lot of efforts were spent in facing the problem of the analysis of the enormous amount of data coming from neuronal population electric recordings from Multi-electrode array (MEA) devices [1, 2]
Starting from the assumption of the repetitiveness of the neuronal spike shape, the data set is simplified as a set of point process allowing the reduction of samples of about one magnitude order [3, 4]
In conclusion we can summarize that the method presented here is a valuable tool for the high frequency neuronal network data analysis
Summary
MATERIALS AND METHODSIn last years a lot of efforts were spent in facing the problem of the analysis of the enormous amount of data coming from neuronal population electric recordings from Multi-electrode array (MEA) devices [1, 2]. These techniques look for repetitive inter-channel spike sequences with complex pattern recognition algorithm, using, for example, neural networks [14] In last years a lot of efforts were spent in facing the problem of the analysis of the enormous amount of data coming from neuronal population electric recordings from Multi-electrode array (MEA) devices
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