Neural spikes digital detector/sorting on FPGA

Vallicelli, Elia A.; Matteis, M. De; Baschirotto, A.; Rescati, Michael; Reato, Marco; Maschietto, Marta; Vassanelli, Stefano; Guarrera, Daniele; Collazuol, G.; Zeiter, R.

doi:10.1109/biocas.2017.8325077

Cited by 10 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparison of the in vivo spike detection state machine performance with offline detection of spikes in signal epochs containing high-amplitude artifacts highlighted the capability to trigger stimulation with a maximum delay of 967 µs and an overall accuracy between 72% and 97% [ 95 ]. Such an approach stands as a reference for closed-loop system design for ADS, overcoming historical limitations of monopolar voltage threshold algorithms [ 96 ] and providing a convenient implementation as a modification to the existing open-source code, thus avoiding the implementation of the full data acquisition circuit such as in [ 97 , 98 , 99 , 100 ]. Moreover, the state machine allows for remarkable flexibility in terms of threshold levels parametrization during ongoing acquisition stages, leading to higher selectivity in the spike detection.…”

Section: Neuromorphic Neuroprostheses: State-of-the-art and Perspectivesmentioning

confidence: 99%

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering

et al. 2022

View full text Add to dashboard Cite

Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that ‘case-study’, we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as ‘brain-prostheses’, capable of rewiring and/or substituting the injured nervous system.

show abstract

Section: Neuromorphic Neuroprostheses: State-of-the-art and Perspectivesmentioning

confidence: 99%

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In this extracellular recording technique, there is a space between the cell membrane and the sensor surface (electrodes) which limits the damage of the cell and allows longterm implants. The biocompatibility is guaranteed by the manufacturing process, that combines a complementary metal-oxide-semiconductor (CMOS) standard technique with a biocompatible metal oxide for the gate [5,6]. The small size and pitch of the grid pixels (electrodes) of the EOSFET MTAs results in a very good spatial resolution; on the other hand, these sensors, being extracellular, provide a limited signal-to-noise ratio (3-6 dB) compared to the standard passive electrodes [7], due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors

et al. 2021

View full text Add to dashboard Cite

Real-time neural spike detection is an important step in understanding neurological activities and developing brain-silicon interfaces. Recent approaches exploit minimally invasive sensing techniques based on implanted complementary metal-oxide semiconductors (CMOS) multi transistors arrays (MTAs) that limit the damage of the neural tissue and provide high spatial resolution. Unfortunately, MTAs result in low signal-to-noise ratios due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end. In this paper we investigate the performance achievable by using spike detection algorithms for MTAs, based on some variants of the smoothed non-linear energy operator (SNEO). We show that detection performance benefits from the correlation of the signals detected by the MTA pixels, but degrades when a high firing rate of neurons occurs. We present and compare different approaches and noise estimation techniques for the SNEO, aimed at increasing the detection accuracy at low SNR and making it less dependent on neurons firing rates. The algorithms are tested by using synthetic neural signals obtained with a modified version of NEUROCUBE generator. The proposed approaches outperform the SNEO, showing a more than 20% increase on averaged sensitivity at 0 dB and reduced dependence on the neuronal firing rate.

show abstract

“…These systems, such as the acquisition system provided by Intan or the Open-Ephys acquisition board (Siegle 2017), use an FPGA to run the SPI that controls the amplifier chip while maintaining a buffer for USB communication with a host computer. Several proposed spike detection and spike sorting techniques take advantage of the FPGA, an integrated circuit that the end-user can reconfigure (Biffi 2010, Gibson 2013, Park 2017, Vallicelli 2017. Implementing the detection and sorting circuit on an FPGA allows the use of neurophysiological spiking as a reliable control signal in real-time, with low-latency; however, most implementations require custom integration with respect to the design of the full data acquisition circuit, which typically varies from laboratory to laboratory.…”

Section: Introductionmentioning

confidence: 99%

Improving an open-source commercial system to reliably perform activity-dependent stimulation

et al. 2019

View full text Add to dashboard Cite

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 fieldprogrammable 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.

show abstract

Neural spikes digital detector/sorting on FPGA

Cited by 10 publications

References 6 publications

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering

Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors

Improving an open-source commercial system to reliably perform activity-dependent stimulation

Contact Info

Product

Resources

About