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Electronics and computational hardware for ultra-high channel count electrophysio

Award Number: R43MH101943
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/06/2014
PERIOD OF PERFORMANCE END DATE: 07/31/2016

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Electronics and computational hardware for ultra-high channel count electrophysio - DESCRIPTION (provided by applicant): 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2017 ( Subtotal = $0 )
2017	2015	LEAFLABS, LLC	288 NORFOLK ST STE 5	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	000	2	12/16/2016	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2015 ( Subtotal = $346,447 )
2015	2015	LEAFLABS LLC	288 NORFOLK STREET	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	000	2	7/29/2015	NON-COMPETING CONTINUATION	$346,447
														Subtotal = $346,447

Issue Date FY: 2014 ( Subtotal = $349,656 )
2014	2014	LEAFLABS LLC	288 NORFOLK STREET	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	000	1	8/6/2014	NEW	$349,656
														Subtotal = $349,656

Grand Total All Awards = $696,103

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Electronics and computational hardware for ultra-high channel count electrophysio

Award Number: R43MH101943

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/06/2014

PERIOD OF PERFORMANCE END DATE: 07/31/2016

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