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Sun et al. Soft Sci. 2025, 5, 18 https://dx.doi.org/10.20517/ss.2024.77 Page 13 of 26

Memristors enable high-density and parallel integration through crossbar array architectures for efficient
and scalable neural network implementations. Their nonvolatile nature ensures data persistence without
continuous power, reducing overall energy consumption and enhancing system reliability. They also
support CIM operations, effectively mitigating the von Neumann bottleneck by performing calculations
directly within the memory matrix, which accelerates processing speeds and lowers energy costs.
Additionally, memristors exhibit low power consumption and minimal heat generation, making them an
ideal option for large-scale, energy-efficient neuromorphic systems. They provide flexible synaptic behavior
through tunable resistance states that can closely mimic the dynamic synaptic connections of biological
brains, which enhances learning and memory capabilities. The simplified device structure of memristors
facilitates easier manufacturing and cost-effective production, while their high scalability and integration
compatibility with existing semiconductor technologies enable seamless incorporation into advanced
neuromorphic architectures as well. For example, the coexistence of negative differential resistance (NDR)
and resistance switching (RS) behaviors in a memristive device with Ag/ZnOx/TiOy/indium tin oxide
(ITO) structure was fabricated to sense temperature and information storage . This multifunctional device
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leveraged the dual properties of NDR and RS to enhance both sensing accuracy and data retention
capabilities. Besides inorganic oxides, polymer-based biomaterials were also used as active layers in the
production of memristors. A polyurethane sponge sensor pressure sensor and chitosan-based memristor
were combined to realize the perception of external signals and the functions of perception, memory, and
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data processing by adjusting the synaptic weight [Figure 5A]. The circuit connection diagram of single
pressure and a memristor is shown in Figure 5B, and the I-V curves, current on/off ratio, and repeated test
performance of chitosan-based memristor are shown in Figure 5C. A pressure sensory array and memristive
spiking neuron array were integrated to acquire stimulation, process and recognize the pressure signals. The
instantaneous spike frequency maps obtained by the memristive spiking neural array were used to train the
SNN, and the neural network successfully extracted and learned the spatial features of spike frequency maps
for different letters, achieving a classification accuracy of approximately 98% [Figure 5D-F]. The
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integration of a pressure sensor and NbOx-based memristor is illustrated in Figure 5G, using the circuit
diagram of spiking nociceptor by switching high resistance state (Roff) and low resistance state (Ron) to
generate spiking signals [Figure 5H], an intelligent sensor is able to detect harmful stimuli [Figure 5I].
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Besides this, the memristor with a Ti/Pt/NbOx/Ti/Pt structure integrated the pressure sensors to act as
multisensory perception. A 3 × 3 array of multimode-fused spiking neuron was fabricated [Figure 5J], and
the circuit diagram of spiking neuron was used to generate the spiking signals [Figure 5K]. Through this
method, temperature and pressure analog information were fused into one spike train; a SNN algorithm
was simulated to recognize objects [Figure 5L].
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NEURAL NETWORK ALGORITHMS
AI learning techniques have been integrated into sensory systems to enhance their ability to adapt, learn,
and make accurate predictions based on the input data. AI learning can be broadly categorized into two
main approaches: global error-driven and local neuroscience-inspired learning. The global error-driven
learning approach focuses on minimizing a global error metric across the entire model. Techniques such as
gradient descent are commonly used, where the system calculates the difference between predicted and
actual outputs and adjusts weights across the entire network to reduce error. Deep learning models, such as
neural networks trained with backpropagation, are prime examples of global learning. Although these
models often require extensive datasets and substantial computational resources, they can achieve high
accuracy in complex tasks.

Inspired by the human brain, local learning focuses on updating the model through mechanisms that utilize
information available at individual neurons or specific layers. Local learning rules, such as Hebbian learning