Real-time edge processing of neural signals with memristive technologies

Abstract

Intracranial brain-computer interfaces, capable of real-time neural activity decoding, present a revolutionary opportunity to improve the quality of life of individuals with dysfunction or damage to the nervous system. Despite recent advancements in neural recording, neuroprosthetic technologies still face bottlenecks in data processing and transmission. Effective neuro-prosthetic devices must deliver enhanced performance metrics including high accuracy, low-power, small size, and minimal latency to enable continuous and real-time brain interfacing. Memristive technologies are promising candidates, acting as bioelectronic links that integrate biosensing with computation for brain-inspired architectures, and operating at low power levels. Memristive devices are two-terminal electronic components that reversibly and gradually adjust conductance in response to electrical stimuli, with their memory state depending on the thresholded integral of the input voltage. Acting as integrating sensors, they suppress noise and encode signal amplitude and frequency within their resistive state when biased with suitable preamplified neuronal signals. This behaviour similar to biological synapses offers a novel solution for processing strategies in brain-computer interfaces. A memristor-based platform for detecting action potentials (APs) -- the fundamental units of communication between neurons and a well-established indicator of brain activity -- has already demonstrated promising results in the literature. This doctoral research proposes a memristor-based processing platform for real-time decoding of neural signals, with a focus on population-level activity rather than single-neuron action potential (AP) detection. In many clinical or assistive applications, such as state monitoring or rehabilitation relying on fine-grained single-neuron activity is not necessary. Instead, larger scale population-level dynamics provide more robust and stable biomarkers. Moreover, relying on these signals offers energy efficiency benefits due to their reduced bandwidth requirements. Specifically, local field potentials (LFPs) were used, as they provide greater spatial coverage and temporal stability by capturing collective synaptic activity. LFPs recorded in vivo from the ventral tegmental area of awake rats performing associative memory tasks were applied to TiOx-based non-volatile memristors, significantly reducing processing power. The system achieved real-time biomarker detection with over 98% accuracy and power consumption as low as 4.14 nW per channel—up to 100× lower than comparable state-of-the-art methods, at similar accuracy levels. This memristor-based protocol was then extended to process the envelope of multi-unit activity (eMUA), a more recently explored neural signal that also reflects population dynamics but enables earlier biomarker detection and reduced inter-channel correlation—key for real-time prosthetic control. With over 95% detection accuracy and ~9 nW power consumption, the approach was validated across different metal-oxide memristor stacks, confirming the platform-agnostic applicability of the MIS method. The integration of MIS with ultra-low-power front-end analogue circuitry showed a 30× reduction in power demand compared to majority of state of the art front-end chips, achieving sub-μW consumption and projecting up to 10× improvement over the most advanced implementations. LFPs emerged as the most power-efficient and reliable neural source in the presented experiments, while eMUA provided a lower-latency alternative better suited to multi-channel applications. As the number of recording channels increases and monolithic integration with CMOS is optimised, this memristor-based strategy is expected to further reduce power consumption per channel while enabling the detection of increasingly complex behavioural states. As a final experiment, given the continued prevalence of action potentials in neural signal processing, a strategy was developed to detect not amplitude-based but frequency-encoded biomarkers from action potential activity. Temporal compression was applied to reduce spiked quantity, while preserving the information needed to distinguish between high- and low-activity brain states—patterns often linked to neurological conditions such as Alzheimer’s disease or stroke. This compression was implemented using volatile metal-oxide memristors, whose intrinsic temporal filtering proved beneficial in identifying regions of high-frequency spiking activity before passing the data to a spiking neural network (SNN) for classification. Once again, neural activity was processed more efficiently and benchmarked against detection accuracy in a clinically relevant in vivo application using anaesthetised rats. These biomarkers were reliably detected using only 10% of the original data, while maintaining an SNN detection accuracy of approximately 97.5%. Overall, this research lays the groundwork for scalable, ultra-low-power systems for chronic neural monitoring and implantable neuro-prosthetic technologies