scholarly journals High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity

2012 ◽  
Vol 6 ◽  
Author(s):  
Felix Franke ◽  
David Jäckel ◽  
Jelena Dragas ◽  
Jan Müller ◽  
Milos Radivojevic ◽  
...  
Keyword(s):  
Real Time ◽  
Neural Plasticity ◽  
Microelectrode Array ◽  
Closed Loop ◽  
Emerging Technology ◽  
High Density ◽  
Spike Sorting ◽  
Array Recordings

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 Estimating the fraction of falsely detected spikes in high density microelectrode array recordings based on correlations

2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Jens-Oliver Muthmann ◽  
Hayder Amin ◽  
Alessandro Maccione ◽  
Evelyne Sernagor ◽  
Luca Berdondini ◽  
...  
Keyword(s):  
Microelectrode Array ◽  
High Density ◽  
Array Recordings

scholarly journals Closed-loop optical stimulation and recording system with GPU-based real-time spike sorting

2014 ◽  
Author(s):  
Ling Wang ◽  
Thoa Nguyen ◽  
Henrique Cabral ◽  
Barbara Gysbrechts ◽  
Francesco Battaglia ◽  
...  
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Recording System ◽  
Spike Sorting ◽  
Optical Stimulation

Real-time signal processing for high-density microelectrode array systems

2009 ◽  
Vol 23 (11) ◽  
pp. 983-998 ◽  
Author(s):  
K. Imfeld ◽  
A. Maccione ◽  
M. Gandolfo ◽  
S. Martinoia ◽  
P.-A. Farine ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Microelectrode Array ◽  
High Density ◽  
Time Signal ◽  
Real Time Signal Processing

scholarly journals Real-Time Analysis of Oxygen Gradient in Oocyte Respiration Using a High-Density Microelectrode Array

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 256
Author(s):  
William Tedjo ◽  
Yusra Obeidat ◽  
Giovana Catandi ◽  
Elaine Carnevale ◽  
Tomas Chen
Keyword(s):  
Real Time ◽  
Oxygen Concentration ◽  
Microelectrode Array ◽  
Temporal Dynamics ◽  
Imaging System ◽  
High Density ◽  
Oxide Semiconductor ◽  
Two Dimensional ◽  
Imaging Results

Physiological events related to oxygen concentration gradients provide valuable information to determine the state of metabolizing biological cells. The existing oxygen sensing methods (i.e., optical photoluminescence, magnetic resonance, and scanning electrochemical) are well-established and optimized for existing in vitro analyses. However, such methods also present various limitations in resolution, real-time sensing performance, complexity, and costs. An electrochemical imaging system with an integrated microelectrode array (MEA) would offer attractive means of measuring oxygen consumption rate (OCR) based on the cell’s two-dimensional (2D) oxygen concentration gradient. This paper presents an application of an electrochemical sensor platform with a custom-designed complementary-metal-oxide-semiconductor (CMOS)-based microchip and its Pt-coated surface MEA. The high-density MEA provides 16,064 individual electrochemical pixels that cover a 3.6 mm × 3.6 mm area. Utilizing the three-electrode configuration, the system is capable of imaging low oxygen concentration (18.3 µM, 0.58 mg/L, or 13.8 mmHg) at 27.5 µm spatial resolution and up to 4 Hz temporal resolution. In vitro oxygen imaging experiments were performed to analyze bovine cumulus-oocytes-complexes cells OCR and oxygen flux density. The integration of a microfluidic system allows proper bio-sample handling and delivery to the MEA surface for imaging. Finally, the imaging results are processed and presented as two-dimensional (2D) heatmaps, representing the dissolved oxygen concentration in the immediate proximity of the MEA. This paper provides the results of real-time 2D imaging of OCR of live cells/tissues to gain spatial and temporal dynamics of target cell metabolism.

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