Spatiotemporal norepinephrine mapping using a high-density CMOS microelectrode array

Lab on a Chip ◽  
2015 ◽  
Vol 15 (20) ◽  
pp. 4075-4082 ◽  
Author(s):  
John B. Wydallis ◽  
Rachel M. Feeny ◽  
William Wilson ◽  
Tucker Kern ◽  
Tom Chen ◽  
...  
Keyword(s):  
Microelectrode Array ◽  
High Density ◽  
Spatiotemporal Resolution ◽  
High Spatiotemporal Resolution ◽  
Electrochemical Imaging

Electrochemical imaging with high spatiotemporal resolution of dynamic norepinephrine distributions is achieved using microfluidics and a high-density CMOS platinum microelectrode array with an on-board potentiostat.

scholarly journals Applicability of independent component analysis on high-density microelectrode array recordings

2012 ◽  
Vol 108 (1) ◽  
pp. 334-348 ◽  
Author(s):  
David Jäckel ◽  
Urs Frey ◽  
Michele Fiscella ◽  
Felix Franke ◽  
Andreas Hierlemann
Keyword(s):  
Microelectrode Array ◽  
Simulated Data ◽  
Complementary Metal Oxide Semiconductor ◽  
Simultaneous Recording ◽  
High Density ◽  
Independent Component ◽  
Oxide Semiconductor ◽  
Spike Sorting ◽  
Spatiotemporal Resolution ◽  
Source Signals

Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as “spike sorting.” For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

scholarly journals High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels

Lab on a Chip ◽  
2015 ◽  
Vol 15 (13) ◽  
pp. 2767-2780 ◽  
Author(s):  
Jan Müller ◽  
Marco Ballini ◽  
Paolo Livi ◽  
Yihui Chen ◽  
Milos Radivojevic ◽  
...  
Keyword(s):  
High Resolution ◽  
Microelectrode Array ◽  
Spatiotemporal Resolution ◽  
High Spatiotemporal Resolution

Novel CMOS-based microelectrode array to enable high-spatiotemporal- resolution access to neuronal preparations on subcellular, cellular, and network level.

Imaging collagen type I fibrillogenesis with high spatiotemporal resolution

2015 ◽  
Vol 149 ◽  
pp. 86-94 ◽  
Author(s):  
Dimitar R Stamov ◽  
Erik Stock ◽  
Clemens M Franz ◽  
Torsten Jähnke ◽  
Heiko Haschke
Keyword(s):  
Collagen Type ◽  
Collagen Type I ◽  
Type I ◽  
Spatiotemporal Resolution ◽  
High Spatiotemporal Resolution
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