Mimicking Pain-Perceptual Sensitization and Pattern Recognition Based on Capacitance- and Conductance-Regulated Neuroplasticity in Neural Network

Bio-Inspired P-type TeSeOx Synaptic Transistor Based on Multispectral Sensing for Neuromorphic Visual Multilevel Nociceptor

The development of neuromorphic color vision has significant research implications in the fields of machine vision and artificial intelligence. By mimicking the processing mechanisms of energy-efficient biological visual systems, it offers a unique potential for real-time color environment perception and dynamic adaptability. This paper reports on a multispectral color sensing synaptic device based on a novel p-type TeSeOx transistor, applied to a neuromorphic visual multilevel nociceptor. Due to the intrinsic properties of TeSeOx, its narrow bandgap allows for multi-wavelength (405, 532, 655 nm) response, and its oxide semiconductor-based persistent photoconductivity converts optical signals into stored electrical signals, successfully emulating key synaptic characteristics such as excitatory postsynaptic current (EPSC), multi-pulse facilitation, and the transition from short-term to long-term memory. Additionally, it simulates learning, forgetting, and relearning behaviors, as well as image memory under tricolor light. Finally, using optical signals as a pain stimulus, the fundamental functions of a nociceptor are realized, including "threshold," "non-adaptation," "relaxation," and "nociceptive sensitization". More importantly, by using tricolor light, multilevel pain perception is acheived. These results have the potential to advance fields such as autonomous driving, machine vision, and intelligent alert systems.

Motion image feature extraction through voltage modulated memory dynamics in an IGZO thin-film transistor

Motion image recognition is a critical component of internet of things (IoT) applications, necessitating advanced processing techniques for spatiotemporal data. Conventional feedforward neural networks (FNNs) often fail to effectively capture temporal dependencies. In this work, we propose an indium gallium zinc oxide (IGZO) thin-film transistor (TFT) gated by a hafnium oxide (HfOx) dielectric layer, exhibiting voltage-modulated fading memory dynamics. The device exhibits transient current responses induced by oxygen vacancy migration, dynamically modulating channel conductance and enabling the transformation of 4-bit time-series sequences into 16 distinct states. This approach enhances the feature extraction process for motion history images by balancing the transient decay of individual frame contributions with the cumulative effect of the motion sequence. Systematic evaluation identifies an optimal pulse height of 2.5 V, achieving a motion direction classification accuracy of 93.9%. In contrast, simulations under non-volatile memory conditions exhibit static retention, leading to symmetric trajectories and significantly lower classification accuracy (49.6%). To further improve temporal data processing, we introduce the degree of state separation (DS) as a metric to quantify state distribution uniformity and identify optimal pulse conditions. This work advances the development of neuromorphic devices for efficient time-series data processing, providing valuable insights into the interplay between fading memory dynamics and neural network performance.

Stretchable hierarchical metal wire networks for neuromorphic emulation of nociception and anti-nociception

Among biomimetic technologies, the incorporation of sensory hardware holds exceptional utility in human-machine interfacing. In this context, devices receptive to nociception and emulating antinociception gain significance as part of pain management. Here we report, a stretchable two-terminal resistive neuromorphic device consisting of a hierarchical Ag microwire network formed using a crack templating protocol. The device demonstrates sensitivity to strain, where the application of strain induces the formation of gaps across active elements, rendering the device electrically open. Following activation by voltage pulses, the device exhibits potentiated states with finite retentions arising from filamentary growth across these gaps due to field migration. Remarkably, the strain-induced functioning alongside controllable gaps enables achieving user-controlled neuromorphic properties, desired for self-adaptive intelligent systems. Interestingly, in the neuromorphic potentiated state, the response to strain is enhanced by ∼106 due to higher sensitivities associated with nanofilaments. The device emulates basic neuromorphic functionalities such as threshold switching, and short-term (STP) and long-term potentiations (LTP). Furthermore, the sensitivity has been exploited in mimicking nociception through strain-induced changes in the potentiated state. Interestingly, repetition of the strain stimulus leads to endurance making the device restore its conductance, thereby emulating adaptation and habituation representing the antinociceptive behavior.