Capable of reflecting the location and intensity of external harmful stimuli, a nociceptor network is of great importance for receiving pain-perception information. However, the hardware-based implementation of a nociceptor network through the use of a transistor array remains a great challenge in the area of brain-inspired neuromorphic applications. Herein, a simple ionotronic junctionless oxide transistor array with pain-perception abilities is successfully realized due to a coplanar-gate proton-coupling effect in sodium alginate biopolymer electrolyte. Several important pain-perception characteristics of nociceptors are emulated, such as a pain threshold, the memory of prior injury, and sensitization behavior due to pathway alterations. In particular, a good graded pain-perception network system has been successfully established through coplanar capacitance and resistance. More importantly, clear polarity reversal of Lorentz-type spatiotemporal pain-perception emulation can be finally realized in our projection-dependent nociceptor network. This work may provide new avenues for bionic medical machines and humanoid robots based on these intriguing pain-perception abilities.

A biopolymer-gated ionotronic junctionless oxide transistor array for spatiotemporal pain-perception emulation in nociceptor network.

Neuromorphic circuits emulating the bio‐brain functionality via artificial devices have achieved a substantial scientific leap in the past decade. However, even with the advent of highly advanced bio‐inspired algorithms, the artificial intelligence based on current neuromorphic circuits is lagging behind significantly when compared with naturally evolved biological neural circuits. This massive and intriguing discrepancy is partly due to the incomprehensive understanding of bio‐brain operating mechanism, which relies heavily on the extremely complexed entangled 3D hierarchical neural networks. Configuring 3D neuromorphic hardware with combined computing and memory functionalities, coupled with compatible progress of software algorithms, can be an inevitable route to surmount the limitation encountered by current 2D artificial circuits. Herein, referring to the neuron configuration in 3D perspective together with detailed signal generation and propagation mechanism, the von Neumann configuration is compared with state‐of‐the‐art in‐memory computing architecture, and the development and perspectives of 3D in‐memory computing neuromorphic circuits are highlighted.

Bio‐Inspired 3D Artificial Neuromorphic Circuits

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies. 

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Progress of Materials and Devices for Neuromorphic Vision Sensors

We show how any finite physical system with morphological, i.e. three-dimensional embedding or shape, degrees of freedom and locally limited free energy will, under the constraints of the free energy principle, evolve over time towards a neuromorphic morphology that supports hierarchical computations in which each ‘level’ of the hierarchy enacts a coarse-graining of its inputs, and dually, a fine-graining of its outputs. Such hierarchies occur throughout biology, from the architectures of intracellular signal transduction pathways to the large-scale organization of perception and action cycles in the mammalian brain. The close formal connections between cone-cocone diagrams (CCCD) as models of quantum reference frames on the one hand, and between CCCDs and topological quantum field theories on the other, allow the representation of such computations in the fully-general quantum-computational framework of topological quantum neural networks.

https://iopscience.iop.org/article/10.1088/2634-4386/aca7de/pdf

The free energy principle induces neuromorphic development

We propose an indium gallium zinc oxide (IGZO) nanofiber based photoelectric synapse. Long-term potentiation and depression emulations are realized by exploiting optical and electrical stimulus as the excitatory and inhibitory inputs, respectively. Significantly, IGZO nanofiber-based photoelectric synapse exhibit multilevel characteristics (up to 10 bits) with low updating energy (~1.0 fJ). Furthermore, an artificial neural network (ANN) based on IGZO nanofiber photoelectric synapse is built and evaluated through simulations. The performance indicates more than 93% accuracy in recognizing the standard MNIST handwritten digits, showing the great potential for high-precision neuromorphic computing by the IGZO nanofiber photoelectric synapse.

Photoelectric Synapse Based on InGaZnO Nanofibers for High Precision Neuromorphic Computing

Indium-Gallium-Zinc-Oxide Based Photoelectric Neuromorphic Transistors for Modulable Photoexcited Corneal Nociceptor Emulation

Recent advances in emerging neuromorphic computing and perception devices

A Spiking Stochastic Neuron Based on Stacked InGaZnO Memristors

/pdf/a-spiking-visual-neuron-for-depth-perceptual-systems-42311g25hy.pdf

Huiwu Mao

Papers

Indium-Gallium-Zinc-Oxide Based Photoelectric Neuromorphic Transistors for Modulable Photoexcited Corneal Nociceptor Emulation

Recent advances in emerging neuromorphic computing and perception devices

A Spiking Stochastic Neuron Based on Stacked InGaZnO Memristors

A Spiking Visual Neuron for Depth Perceptual Systems