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.
The abundance of data to be processed calls for new computing paradigms, which could accommodate, and directly map artificial neural network (ANN) architectures at the hardware level. Neuromorphic computing has emerged as a potential solution, proposing the implementation of artificial neurons and synapses on physical substrates. Conventionally, neuromorphic platforms are deployed in complementary metal-oxide–semiconductor (CMOS) technology. However, such implementations still cannot compete with the highly energy-efficient performance of the brain. This calls for novel ultra-low-power nano-scale devices with the possibility of upscaling for the implementation of complex networks. In this paper, a multi-state spin-orbit torque (SOT) synapse based on the three-terminal perpendicular-anisotropy magnetic tunnel junction (P-MTJ) is proposed. In this implementation, P-MTJs use common heavy metals (HMs) but with different cross-section areas, thereby creating multiple states that can be harnessed to implement synapses. The proposed multi-state SOT synapse can solve the state-limited issue of spin-based synapses. Moreover, it is shown that the proposed multi-state SOT synapse can be programmed to reproduce the spike-timing-dependent plasticity (STDP) learning algorithm.
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