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The Electrophysiology Group aims to monitor and manipulate spikes with high specificity to open up new avenues for understanding the neural underpinnings of behavior.
Goals and Approach
We combine cutting-edge tools for recording electrical activity, such as Neuropixels probes, with reagents for identifying and perturbing specific neuronal cell types in living brains. Our current focus is on improving the reliability and throughput of extracellular electrophysiology experiments. Our goal is to build an extensible platform for rapidly testing hypotheses about how learning, decisions, and actions are implemented in brain-wide neural circuits.
Electrophysiology experiments require mechanisms for guiding electrodes to their targets. We are developing a probe insertion system that improves on existing designs in terms of density, precision, and flexibility. Our goal is to record dozens of interacting brain areas at a time, with each electrode precisely targeted to connected brain regions. The prototype design includes an array of easily swappable modules for recording, visualization, and light delivery. After validation, the system will be made available to the research community.
As the number of brain regions of interest grows, it becomes rapidly more difficult to record from all of them simultaneously. Standard targeting methods, based on skull landmarks, are too variable and imprecise to hit small structures (such as neuromodulatory nuclei) reliably. We are collaborating with Donghoon Lee in the UW Department of Radiology to develop procedures based on high-resolution MRI imaging to improve targeting.
In a typical electrophysiology experiment, deep structures are targeted using stereotaxic coordinates aligned to skull landmarks. This approach has a margin of error of about 0.4 mm, which is too variable to hit small structures (such as neuromodulatory nuclei) reliably. Therefore, we are collaborating with Donghoon Lee in the UW Department of Radiology to obtain high-resolution anatomical MRI volumes for individual mice. We are currently testing whether it is possible to use these volumes to improve electrode placement accuracy.
Advancing our understanding of computation in neural circuits will depend critically on exploring how various cell types interact at the level of the whole brain during behavior. However, the vast majority of electrophysiology datasets lack information about the cell types that were recorded. To address this, we plan to use “optotagging” to obtain the ground truth spiking activity of a variety of genetically defined cell types, then train a model to classify cells based on their electrophysiological properties alone. Such a “Cell Type Lookup Table” would dramatically improve our ability to connect electrophysiology experiments to the vast knowledge of cell types being generated by the Allen Institute for Brain Science and elsewhere.
Our team is supporting and extending a widely used open-source application for acquiring multichannel electrophysiology data. The software was designed around a plugin architecture that makes it easy for scientists to add new functionality. With funding from a BRAIN Initiative U24 award, we are working to make Open Ephys plugins developed by the community accessible to a wider audience.
View the Open Ephys GUI.
