ENHANCING THE YIELD OF HIGH-DENSITY ELECTRODE ARRAYS THROUGH AUTOMATED ELECTRODE SELECTION

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to...

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Bibliographic Details
Published in:International journal of neural systems 2012-02, Vol.22 (1), p.1-19
Main Authors: VAN DIJCK, GERT, SEIDL, KARSTEN, PAUL, OLIVER, RUTHER, PATRICK, VAN HULLE, MARC M., MAEX, REINOUD
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Algorithms
Arrays
Criteria
Electrodes
Electronics
Electrophysiology - instrumentation
Electrophysiology - methods
Interneurons - physiology
Models, Neurological
Neurons
Neurons - physiology
Pyramidal Cells - physiology
Signal-To-Noise Ratio
Sorting
Spikes
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Summary:Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to select electrodes automatically, hereby allowing to reduce the amount of data and locating those electrodes that are close to neurons. The electrodes are selected according to a new penalized signal-to-noise ratio (PSNR) criterion that demotes electrodes from becoming selected if their signals are redundant with previously selected electrodes. It is shown that, using the PSNR, interneurons generating smaller spikes are also selected. We developed a model that aims to evaluate algorithms for electronic depth control, but also generates benchmark data for testing spike sorting and spike detection algorithms. The model comprises a realistic tufted pyramidal cell, non-tufted pyramidal cells and inhibitory interneurons. All neurons are synaptically activated by hundreds of fibers. This arrangement allows the algorithms to be tested in more realistic conditions, including backgrounds of synaptic potentials, varying spike rates with bursting and spike amplitude attenuation.
ISSN:0129-0657
1793-6462
DOI:10.1142/S0129065712003055