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Live spike sorting for multichannel and high-channel recordings

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41NS132700-01A1
Agency Tracking Number: R41NS132700
Amount: $438,501.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 106
Solicitation Number: PA22-178
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-09-22
Award End Date (Contract End Date): 2025-08-31
Small Business Information
3000 Lawrence Street
Denver, CO 80205
United States
DUNS: 078778274
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (303) 556-4924
Business Contact
Phone: (720) 608-9993
Research Institution
AURORA, CO 80045-2571
United States

 Nonprofit College or University

Project Summary:
The goal of this project is to create two prototypes of a novel live spike sorting system which can be used by
investigators to spike sort streams of neural data recorded by multi-channel, high channel and ultra-high
channel probes. In most in-vivo extracellular recording conditions, an electrode can pick up neural spikes
from several nearby neurons resulting in so-called “multi-unit” activity in the recording trace. Spike sorting
algorithms are then used to separate this multi-unit activity into several sets of “single-unit” activities, each
of which represents the action potential firing pattern of a single neuron. This sorting process is typically a
computationally intensive process and is growing into a critical technology gap with the advent of multi and
high channel count hardware. Live spike sorting of a complete set of multichannel data has been challenging
if not impossible. On the other hand, there is a demand for live spike sorting during an experiment,
especially by those investigators who record from functionally heterogenous brain areas such as, for
example, all cortical regions. If an investigator had the ability to review live single cell data, he/she could
determine the quality of the data and adjust the electrode position or decide on next experimental steps
based on the incoming results.
We recently developed the GEMsort algorithm, which, compared to existing spike sorting algorithms, was
designed to sort neural spikes from multichannel probes with immediate sorting outcomes. These
algorithms provide powerful, accurate yet computationally inexpensive spike sorting due to a different
mathematical approach. As a result, these algorithms can spike sort complete streams of complex data,
including data recorded with high channel and ultra-high channel electrodes virtually in real time. In this
proposal, we will develop two tabletop-sized systems based on Field-Programmable Gate Array (FPGA)
technology for laboratory use. These systems will be based on the GEMsort algorithm and add live spike
sorting capabilities to an investigator's existing recording setup.

* Information listed above is at the time of submission. *

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