Optimizing Spike-Sorting Yield through Selection of Optimal Recording Sites in High-density Microelectrode Arrays
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1
ETH Zurich, Department of Biosystems Science and Engineering, Switzerland
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2
ETH Zürich, Department of Biosystems Science and Engineering, Switzerland
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3
Riken, Quantitative Biology Center, Japan
Motivation
Recently, various high-density microelectrode arrays (HDMEA) were developed for in-vitro and in-vivo applications. Some of these arrays feature hundreds to thousands of electrodes, from which a subset can be selected for recording at one time [1-4]. Our goal here is to simultaneously extract the activity of a maximum number of identifiable neurons from such recordings. Careful choice of recording electrodes is required, when the number of recorded neurons exceeds the number of available recording channels. When analyzing data from networks of neurons, one is typically interested in the exact relative timing of the occurrence of action potentials in different neurons. Here, we consider the assignment of recorded action potentials to individual neurons a classification problem. Therefore, we propose a metric relying on linear discriminant analysis (LDA) to compute the expected classification error for a given electrode arrangement. Moreover, we propose an algorithm to automatically find optimal electrode configurations.
Material and Methods
HDMEA: To demonstrate the feasibility of our method, we used a recently developed HDMEA featuring 26,400 Pt microelectrodes, arranged in a grid-like configuration with a center-to-center pitch of 17.5 μm [3]. 1024 of these electrodes can be selected for simultaneous recording by means of low-noise amplifiers (2.4 μV rms in the action potential signal band 300 Hz–10 kHz). Algorithm: The extent to which action potentials from two neurons can be clearly distinguished depends on their multi-electrode waveforms. The best separation can be achieved if information from all electrodes is available. However, only a subset of all available electrodes can be used for recording at the same time. Our method starts out by successively recording through blocks of all 26,400 electrodes, spike sorting each of the recordings, and building templates for each neuron (Step 1). Using the templates from all neurons and linear discriminant analysis [5], we compute the expected spike sorting error (ESSE) using all electrodes. We then compute for each electrode the ESSE under the assumption that the corresponding electrode would not be used for recording (Step 2). Electrodes, the removal of which increases the ESSE by the least amount will then be discarded (Step 3). Step 2 and 3 are iterated until the desired number of electrodes, i.e., the number of available readout channels is reached.
Results
We recorded from a patch of hamster retina for ten minutes per electrode. Based on this data, action potential templates were computed, and the algorithm has been applied to identify the optimal recording electrodes. The classification error decays exponentially upon increasing the number of used electrodes.
Discussion/Conclusion
We have presented a framework to assess the quality of a particular recording scenario. The framework quantifies the error probability for discriminating between all a-priori known templates for all present neurons. The algorithm was applied to recordings from a retina performed with a recently developed HDMEA. Our approach is related to earlier work [6]. However, by relying on linear discriminant analysis, we make - in a Bayesian sense - optimal use of the available recording resources.
[1] Lopez, Carolina M., et al. "A 966-electrode neural probe with 384 configurable channels in 0.13μm SOI CMOS", ISSCC (2016), 392-393.
[2] Viswam, Vijay, et al. "Multi-Functional Microelectrode Array System featuring 59’760 Electrodes, 2048 Electrophysiology Channels, Impedance and Neurotransmitter Measurement Units", ISSCC (2016), 394-396.
[3] Ballini, Marco, et al. "A 1024-Channel CMOS microelectrode array with 26,400 electrodes for recording and stimulation of electrogenic cells in vitro." Solid-State Circuits, IEEE Journal of 49.11 (2014): 2705-2719.
[4] Seidl, Karsten, et al. "CMOS-based high-density silicon microprobe arrays for electronic depth control in intracortical neural recording" Microelectromechanical Systems, Journal of, 20(6):1439– 1448, 2011.
[5] Franke, Felix, et al. "Bayes Optimal Template Matching for Spike Sorting – Combining Fisher Discriminant Analysis with Optimal Filtering, " J. Comput. Neurosci., vol. 38, no. 3, pp. 439–459, 2015.
[6] Vysotska, Olga, "Automatic channel selection and neural signal estimation across channels of neural probes", International Conference on Intelligent Robots and Systems (2014), 1453–1459.
Keywords:
Retina,
CMOS,
Spike-sorting,
high-density microelectrode array (HDMEA)
Conference:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016.
Presentation Type:
Poster Presentation
Topic:
MEA Meeting 2016
Citation:
Müller
J,
Franke
F,
Fiscella
M,
Frey
U,
Bakkum
D and
Hierlemann
A
(2016). Optimizing Spike-Sorting Yield through Selection of Optimal Recording Sites in High-density Microelectrode Arrays.
Front. Neurosci.
Conference Abstract:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays.
doi: 10.3389/conf.fnins.2016.93.00122
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Received:
22 Jun 2016;
Published Online:
24 Jun 2016.
*
Correspondence:
Dr. Jan Müller, ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland, jan.mueller@bsse.ethz.ch