A sub-1&amp;#x00B5;W neural spike-peak detection and spike-count rate encoding circuit

Paraskevopoulou, Sivylla E.; Constandinou, Timothy G.

doi:10.1109/biocas.2011.6107719

Cited by 14 publications

(15 citation statements)

References 12 publications

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“…These methods, by mapping the recorded neural activity to the source active neurons, offer insights in neural coding principles 17 and support novel neuroprosthetic applications 18,19,20,8 . Thus, further advances in the fast developing field of implantable neural interfaces 21 are hampered by key bottlenecks in the processing of neuronal spikes including: a) computational power required to process the ever increasing volume of neural signals (Gb/s range presently) on-node and in real-time 22,23,24,25 , b) bandwidth 26 and, c) scalability.…”

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confidence: 99%

“…The self-resetting mechanism of our devices, ensures that these operate far from their resistive saturation region, which overall enhances the attained spike-detection accuracy. Moreover, we particularly exploit the fact that volatile phenomena are more pronounced at higher resistive states 39,41 reducing the overall power dissipation to less than 100 nW, setting a new state-ofart in spike detectors 25 .…”

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confidence: 99%

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Sub 100 nW Volatile Nano-Metal-Oxide Memristor as Synaptic-Like Encoder of Neuronal Spikes

Gupta

Serb

Khiat

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.

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confidence: 99%

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confidence: 99%

Sub 100 nW Volatile Nano-Metal-Oxide Memristor as Synaptic-Like Encoder of Neuronal Spikes

Gupta

Serb

Khiat

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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“…Spike counting is a method for detecting spikes above a threshold and has been commonly used for Brain-Machine-Interfaces [3]. The front-end system presented herein amplifies the raw EMG signal such that a comparator can be used to detect the spikes using a predefined threshold level.…”

Section: System Overviewmentioning

confidence: 99%

Applying EMG spike and peak counting for a real-time muscle fatigue monitoring system

Dayan

Spulber

Eftekhar

et al. 2012

2012 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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This paper introduces an electromyography (EMG) signal spike-counter system for quantification of muscle fatigue. Through a study of fatiguing leg extension of the rectus femoris (RF) and vastus lateralis (VL) muscles we show that spike and peak counting in EMG data is significantly correlated (>0.85 Rsquared value) with the traditional median frequency (MDF) quantifier for muscle fatigue. This allows for a more computationally efficient and simpler approach using spike counting for an implementation of a real-time muscle fatigue monitoring system.

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“…In previous work [7], we presented a circuit that detects the spike peak magnitude and converts it into a DC voltage. This voltage is the input of the feedback loop (shown in Fig.4) determining the gain of the VGA.…”

Section: B Variable Gain Amplifiermentioning

confidence: 99%

“…Front-end system architecture. Spike peak and magnitude detector detailed previously in [7]. and 100 mV .…”

Section: Introductionmentioning

confidence: 99%

An ultra-low-power front-end neural interface with automatic gain for uncalibrated monitoring

Paraskevopoulou

Constandinou

2012

2012 IEEE International Symposium on Circuits and Systems

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Abstract-This paper presents a dynamic front-end towards achieving unsupervised single-neuron activity monitoring. By implementing at the front-end, an automatic gain control that is optimized for neural signal dynamics, subsequent processing can be achieved without the need for calibration. The system uses three amplification stages (low-noise first stage, variablegain second stage and high-gain third stage), a tuneable highpass filter, and a feedback loop to tune the variable gain. The circuit has been implemented in a commercially-available 0.18 µm CMOS technology with total power consumption between 1.79 and 1.95 µW . The front-end achieves a variable gain from 52 to 86.4 dB with 3 kHz bandwidth and a high-pass filter that is tuneable from 100-300 Hz. The input referred noise is 9.66 µV with a total harmonic distortion of under 1%.

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A sub-1µW neural spike-peak detection and spike-count rate encoding circuit

Cited by 14 publications

References 12 publications

Sub 100 nW Volatile Nano-Metal-Oxide Memristor as Synaptic-Like Encoder of Neuronal Spikes

Sub 100 nW Volatile Nano-Metal-Oxide Memristor as Synaptic-Like Encoder of Neuronal Spikes

Applying EMG spike and peak counting for a real-time muscle fatigue monitoring system

An ultra-low-power front-end neural interface with automatic gain for uncalibrated monitoring

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