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

Abstract

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.

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

“…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.…”

“…The reset mechanism is controlled by voltage biases V C and V D , which are a means to alter the spiking pattern. Negative and positive pulses are harnessed at the network level to activate the synaptic circuits whenever a pre-or postsynaptic spike is elicited [37].…”

“…The ratio between the sizing of M E 1 and that of M E 3 drives the time course of I syn , resulting in a fast onset followed by a slow decaying phase (corresponding to the recharge of C syn after spike completion). The EPSC circuit response has been derived analytically in [37], which also illustrates the relationship between I syn peak value and the synaptic weight V w .…”

“…These features also make them an appealing candidate for implantable neural interfaces, as their structure is inherently suited to simulating spiking neural networks, that are a powerful tool to solve problems of spatiotemporal pattern recognition [24][25][26][27]. As such, neuromorphic systems have already found successful application for processing a broad range of biological electrical signals [28][29][30][31][32][33], including high-frequency oscillations as pathological biomarkers of epilepsy [34][35][36] and seizure detection [37].…”

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

“…23 shows that although it is theoretically conceivable to pursue such a strategy, there are a number of difficulties and factors to take into consideration. [329][330][331] Utilizing specialized hardware designs and components, neuromorphic computing systems seek to replicate the composition and operation of the human brain. Co-engineering the hardware stack entails creating specialized processing units, optimizing the interconnects, and building the neural network architecture.…”