TY - JOUR
T1 - Improving an open-source commercial system to reliably perform activity-dependent stimulation
AU - Murphy, Maxwell
AU - Buccelli, Stefano
AU - Bornat, Yannick
AU - Bundy, David
AU - Nudo, Randolph
AU - Guggenmos, David
AU - Chiappalone, Michela
N1 - Publisher Copyright:
© 2019 IOP Publishing Ltd.
PY - 2019/10/29
Y1 - 2019/10/29
N2 - Objective. Activity-dependent stimulation (ADS) is designed to strengthen the connections between neuronal circuits and therefore may be a promising tool for promoting neurophysiological reorganization following a brain injury. To successfully perform this technique, two criteria must be met: (1) spikes in the extracellular electrical field potential must be detected accurately at one site of interest, and (2) stimulation pulses generated at fixed (<1 ms jitter), low-latency (<10 ms) intervals relative to each detected spike must be delivered reliably to a second site of interest. Here, we aimed to improve noise rejection in a low-cost commercial system to reliably perform ADS in awake, behaving rats, while maintaining latency requirements. Approach. We implemented a spike detection state machine on a field-programmable gate array (FPGA). Because the accuracy of spike detection can be heavily reduced in awake and behaving animals due to biological artifacts such as movement and chewing, the state machine tracks candidate spike waveforms, checking them against multiple programmable thresholds and rejecting any spikes that fail to meet a programmed threshold criterion. Main Results. A series of offline analyses showed that our implementation was able to appropriately trigger stimulation during epochs of biological artifacts with an overall accuracy between 72% and 97%, fixed computational latency of 167 µs, and an algorithmic latency of 300 µs to 800 µs. Significance. Our improvements have been made open-source and are freely available to all scientists working on closed-loop neuroprosthetic devices. Importantly, the improvements are easily incorporated into existing workflows that utilize the Intan Stimulation and Recording Controller.
AB - Objective. Activity-dependent stimulation (ADS) is designed to strengthen the connections between neuronal circuits and therefore may be a promising tool for promoting neurophysiological reorganization following a brain injury. To successfully perform this technique, two criteria must be met: (1) spikes in the extracellular electrical field potential must be detected accurately at one site of interest, and (2) stimulation pulses generated at fixed (<1 ms jitter), low-latency (<10 ms) intervals relative to each detected spike must be delivered reliably to a second site of interest. Here, we aimed to improve noise rejection in a low-cost commercial system to reliably perform ADS in awake, behaving rats, while maintaining latency requirements. Approach. We implemented a spike detection state machine on a field-programmable gate array (FPGA). Because the accuracy of spike detection can be heavily reduced in awake and behaving animals due to biological artifacts such as movement and chewing, the state machine tracks candidate spike waveforms, checking them against multiple programmable thresholds and rejecting any spikes that fail to meet a programmed threshold criterion. Main Results. A series of offline analyses showed that our implementation was able to appropriately trigger stimulation during epochs of biological artifacts with an overall accuracy between 72% and 97%, fixed computational latency of 167 µs, and an algorithmic latency of 300 µs to 800 µs. Significance. Our improvements have been made open-source and are freely available to all scientists working on closed-loop neuroprosthetic devices. Importantly, the improvements are easily incorporated into existing workflows that utilize the Intan Stimulation and Recording Controller.
KW - FPGA
KW - neuroprosthetics
KW - spike detection
UR - http://www.scopus.com/inward/record.url?scp=85074304621&partnerID=8YFLogxK
U2 - 10.1088/1741-2552/ab3319
DO - 10.1088/1741-2552/ab3319
M3 - Article
C2 - 31315090
AN - SCOPUS:85074304621
SN - 1741-2560
VL - 16
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 6
M1 - 066022
ER -