Tunable Voltage-Mode Subthreshold CMOS Neuron

Ronchini, Margherita; Zamani, Mohsen; Farkhani, Hooman; Moradi, Farshad

doi:10.1109/isvlsi49217.2020.00053

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…The neuron, depicted in figure 3(b), was initially proposed in [45] and further described in [37]. The circuit replicates biologically plausible dynamics, following the Izhikevich model.…”

Section: Net-ten Implementationmentioning

confidence: 99%

NET-TEN: a silicon neuromorphic network for low-latency detection of seizures in local field potentials

et al. 2023

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Objective: Therapeutic intervention in neurological disorders still relies heavily on pharmacological solutions, while the treatment of patients with drug resistance remains an unresolved issue. This is particularly true for patients with epilepsy, 30% of whom are refractory to medications. Implantable devices for chronic recording and electrical modulation of brain activity have proved a viable alternative in such cases. To operate, the device should detect the relevant electrographic biomarkers from Local Field Potentials (LFPs) and determine the right time for stimulation. To enable timely interventions, the ideal device should attain biomarker detection with low latency while operating under low power consumption to prolong battery life. Approach: Here we introduce a fully-analog neuromorphic device implemented in CMOS technology for analyzing LFP signals in an in vitro model of acute ictogenesis. Neuromorphic networks have progressively gained a reputation as low-latency low-power computing systems, which makes them a promising candidate as processing core of next-generation implantable neural interfaces.Main results: The developed system can detect ictal and interictal events with ms-latency and with high precision, consuming on average 3.50 nW during the task.Significance: The work presented in this paper paves the way to a new generation of brain implantable devices for personalized closed-loop stimulation for epilepsy treatment.

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“…The neuron, depicted in figure 3(b), was initially proposed in [45] and further described in [37]. The circuit replicates biologically plausible dynamics, following the Izhikevich model.…”

Section: Net-ten Implementationmentioning

confidence: 99%

NET-TEN: a silicon neuromorphic network for low-latency detection of seizures in local field potentials

et al. 2023

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“…TCADICS'22 [12] ISCAS'22 [7] TCAS-II'21 [18] ISVLSI'20 [14] MWCAS'20 (V spike ) generated by fast BTBT based integrator N 2 and low threshold non inverting Schmitt trigger (S 2 ). The threshold detector (S 0 ), is wide hysteresis non inverting Schmitt trigger with V UTP 0 = 260mV.…”

Section: Referencementioning

confidence: 99%

A Compact Low Power Multi-mode Spiking Neuron using Band to Band Tunneling

Kadam,

Singh,

Somappa

et al. 2024

Preprint

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Efficient and compact neurons with low power consumption are crucial when designing large-scale spiking neural networks (SNNs) for hardware implementation. Many architectures in the literature showcase different spike patterns associated with biological neurons. However, using bulky capacitors to generate the different time constants related to complex neuron patterns makes these circuits area inefficient. This paper presents a band-to-band-tunneling (BTBT) based energy-efficient and compact neuron capable of producing various spike patterns. The BTBT region's extremely low current enables different time constants while eliminating the need of bulky capacitors. The circuit is based on the Izhikevich neuron model. The proposed circuit is designed in Silicon on Insulator technology to exhibit important firing patterns observed in the biological cortex, viz. regular spiking, fast-spiking, and chattering, and it is fine-tuned for efficient operation at low subthreshold voltages. This circuit utilizes only 129 μm 2 area and consumes only 6.7 fJ energy per spike ( approximately 40% lower area and energy per spike than state-of-the-art multi-mode neurons) in G45RFSOI technology.

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“…To meet the energy requirements posed by the implantability goal, it was decided to use a neuron circuit that operates in the subthreshold regime, meaning, where the gate-to-source voltage driving each transistor is lower than the transistor threshold. In particular, the multiple-variable model introduced in [28] was selected, by virtue of its small silicon area, realistic time constant, and ability to replicate the different firing patterns of the cortical neurons. Since the supply voltage is set to V DDL = 200 mV, the entire circuit operates in the subthreshold region.…”

Section: Neuronmentioning

confidence: 99%

“…A comprehensive analytical description of the equations governing the system is provided in [28], while here the operating principles of the circuit are analyzed only intuitively. Assuming that the neuron circuit is stimulated by a current I syn , the current is integrated by C v onto V mem .…”

Section: Neuronmentioning

confidence: 99%

See 1 more Smart Citation

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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Tunable Voltage-Mode Subthreshold CMOS Neuron

Cited by 10 publications

References 22 publications

NET-TEN: a silicon neuromorphic network for low-latency detection of seizures in local field potentials

NET-TEN: a silicon neuromorphic network for low-latency detection of seizures in local field potentials

A Compact Low Power Multi-mode Spiking Neuron using Band to Band Tunneling

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

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