Written by Dr. Justin Kinney, LeafLabs, Member of Technical Staff
LeafLabs would like to announce that the National Institute of Mental Health has awarded us a National Institute of Health (NIH) grant under Award Number R44MH114783. The award provides the first year of funding for a Phase II Small Business Innovation Research (SBIR) grant that amounts to $1.99M of funding over three years. The grant will fund a collaboration between LeafLabs, Ken Shepard at Columbia University, and Ed Boyden at MIT to build a 1000-channel silicon probe, for freely-moving neural recording, and implement hardware and software solutions for scalable data analysis.
Simultaneous recording and stimulation of larger populations of neurons distributed throughout the brain is needed to rigorously evaluate theories of neural computation at the cellular level in mammals. Previously, we introduced close-packed silicon probes and a direct-to-disk data acquisition architecture to enable 1000-channel neural recording in head-fixed animals. Through pilot studies we demonstrated the successful recording of terabytes of neural spiking activity, but also discovered the shortcomings of the architecture. Two design elements in particular were limiting. First, our head stages were too bulky for freely-moving experiments. Second, our acquisition hardware did not have the ability to quickly analyze all 1000 channels of data. As a result, it took from days to weeks to understand the neural activity content of the terabyte-size recordings. For ultra-high-channel count neural recordings to become routine, the acquisition architecture must allow for and facilitate common experimental preparations (such as freely moving and chronic recordings) and rapid online and offline analysis of large amounts of data.
Accordingly, we propose a 1000-channel silicon probe for freely-moving electrophysiology experiments in combination with a data acquisition system optimized for easy data analysis. The novel silicon probe will simultaneously record and optionally stimulate 1000 closed-packed sites, be compact enough for freely-moving experiments in rodents, and reduce headstage cost by a factor of 10, down to $1 per channel. Furthermore, the re-designed acquisition hardware will not only capture 1000 channels of neural data and store to solid-state drive over a high-speed bus, but will now also copy the data to a GPU and RAM for spike sorting and visualization both online and offline. To test the system, we will perform 1000-channel freely-moving neural recordings in rodents, in collaboration with other labs with expertise.
Ultimately, our goal is for researchers to be able to routinely perform 1000-channel freely-moving experiments in the morning, and have preliminary analytical results by the afternoon. This would amount to a monumental advance in capability and workflow integration that has potential to revolutionize the state of electrophysiology in neuroscience.
