Since its introduction more than 30 years ago, the patch clamp has assumed a central place in cellular neurobiology: it allows for the measurement of suprathreshold and subthreshold synaptic- and ion channel-mediated events, with high signal quality and temporal fidelity; the manipulation of the subcellular processes that shape these events; and the physiological, morphological, and molecular identification of recorded cells. Most of the advances patch clamp has made possible have come in reduced preparations (cell cultures and brain slices), in part because of the challenge of obtaining patch clamp recordings in vivo, even when animals are anesthetized. Patch clamp in vivo is not conceptually difficult, but it requires an experimenter to monitor a living animal while executing a complex series of actions. The challenge is heightened when the animal is not merely living but awake and behaving and when the experimenter must also contend with behavioral equipment (e.g., cameras, locomotion and eye position monitors, virtual reality screens). These kinds of experiments place considerable and (for some) unreasonable demands on experimenter training, labor, expertise, and fortitude; and these demands in turn may limit the yield and quality of the resulting data.

With these concerns in mind, we developed a fully automated system for patch clamp recording in awake, behaving, head-fixed mice (Desai et al., J. Neurophysiol., 2015). The motivation was to move burdens off of human experimenters and onto computers and other pieces of electronic equipment. The goal was to create a single, complete system that could – with a minimum of human intervention – obtain whole cell patch recordings from neurons in behaving mice, while controlling and/or monitoring all relevant behavioral equipment. The result is a system that can do these things with a single button press on a computer screen.

Other scientists have had similar ideas. Most notably, Kodandaramaiah and colleagues (Nat. Methods, 2012) did the pioneering work on automating patch clamp recordings in vivo. Their laboratories (led by Craig Forest at Georgia Tech and Ed Boyden at MIT) continue to provide benchmark innovations in automated patch clamping (www.autopatcher.org). Our own system, we believe, represents an attractive option for scientists who work with awake animals or prefer the Matlab programming environment. We designed our system with awake experiments in mind and, consequently, it allows for easy integration of behavioral equipment. And everything is written in the Matlab scripting language, which has an enormous presence in the neuroscience community.

Our system was described in detail in a paper that was published in the Journal of Neurophysiology (Desai et al., J. Neurophysiol. 114:1331-45, 2015). If your institution does not provide you with access to the journal (jn.physiology.org), you can obtain a copy by pushing the green button on the front page of this website (https://clm.utexas.edu/robotpatch). (Pro-tip: when offered an opportunity to push a green button, always do so. You will not be disappointed.)

Our software itself, including documentation, can be downloaded directly from this site by going to the Download page. Questions, comments, criticisms, and (of course) praise can be directed to the paper’s first author, Niraj S. Desai (desai@utexas.edu).