The advent of in vivo multielectrode recording has indicated the importance of recording from large populations of neurons. As a result, there is much interest in creating new kinds of in vivo multielectrode arrays, including the polytrode, new kinds of microfabricated electrode arrays, and new kinds of ultradense 3-D electrode array. And yet, innovation on the back-end systems for amplifying digitizing, storing, and analyzing extracellular electrophysiological recordings has remained limited, even though these systems often comprise one of the most expensive components of the entire enterprise. Accordingly, we are working to develop a system of advanced electronics and computational hardware to fill the gap in data acquisition systems for ultra high channel count probes, or alternatively to reduce the cost of neural data recording by an order of magnitude in the next two years - the equivalent of a six-fold speedup in the "Moore's law" of the field. Our system, dubbed "Wired-Leaf," currently being prototyped in collaboration with the Boyden Lab at MIT, is a radically new kind of minimalist computer that overcomes several drawbacks associated with existing commercially available systems. In particular, current designs rely upon obsolete architectures and depend on computational systems that are loaded with unnecessary features, while skimping on the raw resources required to acquire and process neural data. Our near-optimally simple and scalable devices directly amplify and digitize data, store it directly to a data storage drive, then transmi it to downstream computers for analysis, all at an order of magnitude lower cost than what is commercially available currently. While our current prototype validates this approach, the focus of this proposal is to polish our current system for marketability and widespread use.

Public Health Relevance

Electrophysiological recording systems allow direct observation of neural activity in animal subjects. This facilitates the study of crucial neuroscientific topics such as development, learning and memory, and cognition, as well as brain diseases such as Alzheimer's, epilepsy, Parkinson's, and depression. Currently, these systems are expensive luxury items for research laboratories, but LeafLabs plans to commoditize them by developing new, cheaper, and higher density electronics for amplifying, digitizing, storing, and analyzing neural data.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43MH101943-01A1
Application #
8715028
Study Section
Special Emphasis Panel (ZRG1-IMST-S (12))
Program Officer
Grabb, Margaret C
Project Start
2014-08-06
Project End
2016-07-31
Budget Start
2014-08-06
Budget End
2015-07-31
Support Year
1
Fiscal Year
2014
Total Cost
$349,656
Indirect Cost
Name
Leaflabs, LLC
Department
Type
DUNS #
078625018
City
Cambridge
State
MA
Country
United States
Zip Code
02139