Microelectrode arrays for neural interface devices that are made of biocompatible thin-film polymer are expected to have extended functional lifetime because the flexible material may minimize adverse tissue response caused by micromotion. However, their flexibility prevents them from being accurately inserted into neural tissue. This article demonstrates a method to temporarily attach a flexible microelectrode probe to a rigid stiffener using biodissolvable polyethylene glycol (PEG) to facilitate precise, surgical insertion of the probe. A unique stiffener design allows for uniform distribution of the PEG adhesive along the length of the probe. Flip-chip bonding, a common tool used in microelectronics packaging, enables accurate and repeatable alignment and attachment of the probe to the stiffener. The probe and stiffener are surgically implanted together, then the PEG is allowed to dissolve so that the stiffener can be extracted leaving the probe in place. Finally, an in vitro test method is used to evaluate stiffener extraction in an agarose gel model of brain tissue. This approach to implantation has proven particularly advantageous for longer flexible probes (>3 mm). It also provides a feasible method to implant dual-sided flexible probes. To date, the technique has been used to obtain various in vivo recording data from the rat cortex.

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible.