Dataset Information

Fast, scalable, Bayesian spike identification for multi-electrode arrays.

ABSTRACT: We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human interaction plays a key role in our method; but effort is minimized and streamlined via a graphical interface. We illustrate our method on data from guinea pig retinal ganglion cells and document its performance on simulated data consisting of spikes added to experimentally measured background noise. We present several tests demonstrating that the algorithm is highly accurate: it exhibits low error rates on fits to synthetic data, low refractory violation rates, good receptive field coverage, and consistency across users.

SUBMITTER: Prentice JS

PROVIDER: S-EPMC3140468 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Fast, scalable, Bayesian spike identification for multi-electrode arrays.

Prentice Jason S JS Homann Jan J Simmons Kristina D KD Tkačik Gašper G Balasubramanian Vijay V Nelson Philip C PC

PloS one 20110720 7

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes thi ...[more]

PMID: 21799725

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Project description:BackgroundMulti-electrode arrays (MEAs) have become popular tools for recording spontaneous and evoked electrical activity of excitable tissues. The majority of previous studies of synaptic transmission in brain slices employed MEAs with planar electrodes that had limited ability to detect signals coming from deeper, healthier layers of the slice. To overcome this limitation, we used three-dimensional (3D) MEAs with tip-shaped electrodes to probe plasticity of field excitatory synaptic potentials (fEPSPs) in the CA1 area of hippocampal slices of 129S5/SvEvBrd and C57BL/6J-TyrC-Brd mice.ResultsUsing 3D MEAs, we were able to record larger fEPSPs compared to signals measured by planar MEAs. Several stimulation protocols were used to induce long-term potentiation (LTP) of synaptic responses in the CA1 area recorded following excitation of Schäffer collateral/commissural fibres. Either two trains of high frequency tetanic stimulation or three trains of theta-burst stimulation caused a persistent, pathway specific enhancement of fEPSPs that remained significantly elevated for at least 60 min. A third LTP induction protocol that comprised 150 pulses delivered at 5 Hz, evoked moderate LTP if excitation strength was increased to 1.5x of the baseline stimulus. In all cases, we observed a clear spatial plasticity gradient with maximum LTP levels detected in proximal apical dendrites of pyramidal neurones. No significant differences in the manifestation of LTP were observed between 129S5/SvEvBrd and C57BL/6J-TyrC-Brd mice with the three protocols used. All forms of plasticity were sensitive to inhibition of N-methyl-D-aspartate (NMDA) receptors.ConclusionPrincipal features of LTP (magnitude, pathway specificity, NMDA receptor dependence) recorded in the hippocampal slices using MEAs were very similar to those seen in conventional glass electrode experiments. Advantages of using MEAs are the ability to record from different regions of the slice and the ease of conducting several experiments on a multiplexed platform which could be useful for efficient screening of novel transgenic mice.

Dataset Information

Fast, scalable, Bayesian spike identification for multi-electrode arrays.

Publications

Fast, scalable, Bayesian spike identification for multi-electrode arrays.

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