Starch-Glycerol-Based Hydrogel Memristors for Bio-Inspired Auditory Neuron Applications

Abstract

In the era of artificial intelligence, the demand for rapid and efficient data processing is growing, and traditional computing architectures are increasingly struggling to meet these needs. Against this backdrop, memristor devices, capable of mimicking the computational functions of brain neural networks, have emerged as key components in neuromorphic systems. Despite this, memristors still face many challenges in biomimetic functionality and circuit integration. In this context, a starch-glycerol-based hydrogel memristor was developed using starch as the dielectric material. The starch-glycerol-water mixture employed in this study has been widely recognized in literature as a physically cross-linked hydrogel system with a three-dimensional network, and both high water content and mechanical flexibility. This memristor demonstrates a high current switching ratio and stable threshold voltage, showing great potential in mimicking the activity of biological neurons. The device possesses the functionality of auditory neurons, not only achieving artificial spiking neuron discharge but also accomplishing the spatiotemporal summation of input information. In addition, we demonstrate the application capabilities of this artificial auditory neuron in gain modulation and in the synchronization detection of sound signals, further highlighting its potential in neuromorphic engineering applications. These results suggest that starch-based hydrogel memristors offer a promising platform for the construction of bio-inspired auditory neuron circuits and flexible neuromorphic systems.

Keywords: artificial auditory neuron; gain modulation; hydrogel; memristor; spatiotemporal integration; starch–glycerol; synchronization detection.

Figure 1

(a) Structural diagram of the Al/starch/Ag/starch/ITO memristor. (b) Cross-sectional SEM image of the device. From top to bottom: Al top electrode (~103 nm), upper starch layer (~80 nm), Ag layer (~265 nm), lower starch layer (~80 nm), ITO (~210 nm), and the glass substrate. (c) Top-view SEM image of the upper starch film, demonstrating a uniform and compact surface morphology. (d) UV–visible absorption spectrum of starch.

Figure 2

(a) I–V characteristic curve of the Al/starch/Ag/starch/ITO device, (b) switching current ratio, (c) I–V characteristic curve of 50 cycles, (d) device durability, (e) cumulative probability of threshold voltage distribution, and (f) threshold voltage distribution of the device.

Figure 3

Conductive mechanism of the device. (a) Relationships between resistance and temperature. (b) Conductive mechanism model of the device.

Figure 4

Memristor-based artificial neuron construction. (a) Schematic diagram of artificial neurons. (b) Spike discharge behavior of artificial neurons. The pulse and circuit parameters were adjusted to adjust the characteristics of spiking neurons: (c) change in pulse amplitude, (d) change in pulse width, (e) change in pulse interval, (f) change in resistance, (g) change in capacitance, and (h) change in input frequency.

Figure 5

Spatiotemporal integration of two pulse sequences at different time differences. (a) Schematic diagram of spatiotemporal integration. (b) Δt = 2.2 ms (c) Δt = −2.2 ms. (d) Δt = 0. Artificial auditory neurons realize neuron gain modulation. (e) Schematic diagram of neuron gain modulation. (f) The function of the number of neuron pulses with respect to the driving input frequency.

Figure 6

Neuron output with different modulation input amplitudes: (a) amplitude of 0 V, (b) amplitude of 0.4 V, (c) amplitude of 0.8 V, (d) amplitude of 1.2 V. Synchronous detection based on artificial auditory neurons: (e) schematic diagram of the input pulse, (f) neuron output when the input is synchronized, and (g) neuron output when the input is not synchronized.