Flexible Energy-Efficient Implementation of Adaptive Spiking Encoder for Neuromorphic Processors

Zamani, Mohsen; Ronchini, Margherita; Huynh, Hai Au; Farkhani, Hooman; Moradi, Farshad

doi:10.1109/iscas51556.2021.9401103

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…The way information is expressed and processed is where analog and digital differ most. [35][36][37] Digital signals are discrete and have a finite number of values, but analog signals are continuous and can theoretically have an endless number of values. Advantages of digital signals include improved precision, resistance to noise, and the capacity to store and analyze significant volumes of data.…”

Section: Alexander Fedotovmentioning

confidence: 99%

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Prakash,

Gupta,

Mehta

et al. 2023

Mater. Adv.

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Section: Alexander Fedotovmentioning

confidence: 99%

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Prakash,

Gupta,

Mehta

et al. 2023

Mater. Adv.

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“…The AEC embedded in this device has already been extensively described in [25]. The peculiarity of this converter resides in the possibility to select among different encoding algorithms.…”

Section: Analog-to-event Convertermentioning

confidence: 99%

A CMOS-based neuromorphic device for seizure detection from LFP signals

Ronchini¹,

Zamani²,

Huynh³

et al. 2021

J. Phys. D: Appl. Phys.

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Recent research provides examples of neuromorphic systems applied to process biological signals or to interface biological tissues. Usually, in such contexts, the neuromorphic system is used for automatic anomaly detection. The automation of the long-term monitoring of biological signals holds promise for lightening the burden placed on clinicians. At the same time, the adoption of such devices potentially allows processing to be performed locally, without the need to transfer data to an external processor. In turn, on-site signal analysis makes closed-loop intervention feasible, to correct the source of the anomalies. So far, the common approach has been to deploy the network of spiking neurons either on multi-core neuromorphic platforms or on programmable units (field programmable gate array). However, if the aim is to develop wearable or even chronically implantable devices, it is imperative to move in the direction of embedded solutions, tailored and optimized for the specific application. To this end, the present study proposes a neuromorphic device implemented in CMOS technology for the detection of epileptic seizures (ictal events) from local field potential (LFP) signals. The LFP data have been acquired by a multi-electrode array in a slice of mouse hippocampus-cortex. The system includes an analog-to-event converter (AEC) encoding the recorded signals into trains of spikes, and a small spiking neural network (SNN) of 2 × 1 neurons with online biological-plausible learning. The AEC yields two spike-trains: UP spikes that account for the positive slope signal and DOWN spikes that correspond to the negative slope signal. The synapses among the input and output layers are plastic and follow the spike-timing-dependent plasticity rule. Early results show that the SNN module is able to detect ictal events with a delay of 64.98 ± 30.92 ms, consuming < 50 pW. The layout of the entire system occupies 528 µm × 278 µm.

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A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface

et al. 2023

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Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for traditional systems. Here, we propose a highly efficient neuromorphic physiological signal processing system based on VO2 memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO2 memristors is utilized in compact Leaky Integrate and Fire (LIF) and Adaptive-LIF (ALIF) neurons, which are incorporated into a decision-making Long short-term memory Spiking Neural Network. The system demonstrates superior computing capabilities, needing only small-sized LSNNs to attain high accuracies of 95.83% and 99.79% in arrhythmia classification and epileptic seizure detection, respectively. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces.

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Flexible Energy-Efficient Implementation of Adaptive Spiking Encoder for Neuromorphic Processors

Cited by 4 publications

References 22 publications

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

A CMOS-based neuromorphic device for seizure detection from LFP signals

A neuromorphic physiological signal processing system based on VO2 memristor for next-generation human-machine interface

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