Dataset Information

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

ABSTRACT: Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

SUBMITTER: Mena GE

PROVIDER: S-EPMC5703587 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

Mena Gonzalo E GE Grosberg Lauren E LE Madugula Sasidhar S Hottowy Paweł P Litke Alan A Cunningham John J Chichilnisky E J EJ Paninski Liam L

PLoS computational biology 20171113 11

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporaril ...[more]

PMID: 29131818

Similar Datasets

Project description:BackgroundThe lack of understanding of fascicular organisation in peripheral nerves limits the potential of vagus nerve stimulation therapy. Two promising methods may be employed to identify the functional anatomy of fascicles within the nerve: fast neural electrical impedance tomography (EIT), and penetrating multi-electrode arrays (MEA). These could provide a means to image the compound action potential within fascicles in the nerve.New methodWe compared the ability to localise fascicle activity between silicon shanks (SS) and carbon fibre (CF) multi-electrode arrays and fast neural EIT, with micro-computed tomography (MicroCT) as an independent reference. Fast neural EIT in peripheral nerves was only recently developed and MEA technology has been used only sparingly in nerves and not for source localisation. Assessment was performed in rat sciatic nerves while evoking neural activity in the tibial and peroneal fascicles.ResultsRecorded compound action potentials were larger with CF compared to SS (∼700 μV vs ∼300 μV); however, background noise was greater (6.3 μV vs 1.7 μV) leading to lower SNR. Maximum spatial discrimination between Centres-of-Mass of fascicular activity was achieved by fast neural EIT (402 ± 30 μm) and CF MEA (414 ± 123 μm), with no statistical difference between MicroCT (625 ± 17 μm) and CF (p > 0.05) and between CF and EIT (p > 0.05). Compared to CF MEAs, SS MEAs had a lower discrimination power (103 ± 51 μm, p < 0.05).Comparison with existing methodsEIT and CF MEAs showed localisation power closest to MicroCT. Silicon MEAs adopted in this study failed to discriminate fascicle location. Re-design of probe geometry may improve results.ConclusionsNerve EIT is an accurate tool for assessment of fascicular position within nerves. Accuracy of EIT and CF MEA is similar to the reference method. We give technical recommendations for performing multi-electrode recordings in nerves.

Dataset Information

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

Publications

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets