Electrode pooling: How to boost the yield of switchable silicon probes for neuronal recordings
Lee, K. H.; Ni, Y.-L.; Meister, M.
Show abstract
State-of-the-art silicon probes for electrical recording from neurons have thousands of recording sites. However, due to volume limitations there are typically many fewer wires carrying signals off the probe, which restricts the number of channels that can be recorded simultaneously. To overcome this fundamental constraint, we propose a novel method called electrode pooling that uses a single wire to serve many recording sites through a set of controllable switches. Here we present the framework behind this method and an experimental strategy to support it. We then demonstrate its feasibility by implementing electrode pooling on the Neuropixels 1.0 electrode array and characterizing its effect on signal and noise. Finally we use simulations to explore the conditions under which electrode pooling saves wires without compromising the content of the recordings. We make recommendations on the design of future devices to take advantage of this strategy.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- CUSP: Complex Spike Sorting from Multi-electrode Array Recordings with U-net Sequence-to-Sequence Prediction 94%
- Flexible modeling of large-scale neural network stimulation: electrical and optical extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) 94%
- Adaptive Spike-Artifact Removal from Local Field Potentials Uncovers Prominent Beta and Gamma Band Neuronal Synchronization 94%
Similar papers in this journal
- Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments 94%
- Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array 94%
- An open-source, ready-to-use and validated ripple detector plugin for the Open Ephys GUI 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.