Light-Emitting Artificial Synapses for Neuromorphic Computing

Nature-Inspired Intelligent Computing: A Comprehensive Survey

Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms. Over the past decades, these paradigms have revealed effective and flexible solutions to practical and complex problems. This paper summarizes the natural mechanisms of diverse advanced nature-inspired intelligent computing paradigms, which provide valuable lessons for building general-purpose machines capable of adapting to the environment autonomously. According to the natural mechanisms, we classify nature-inspired intelligent computing paradigms into 4 types: evolutionary-based, biological-based, social-cultural-based, and science-based. Moreover, this paper also illustrates the interrelationship between these paradigms and natural mechanisms, as well as their real-world applications, offering a comprehensive algorithmic foundation for mitigating unreasonable metaphors. Finally, based on the detailed analysis of natural mechanisms, the challenges of current nature-inspired paradigms and promising future research directions are presented.

Heterointerface engineering of layered double hydroxide/MAPbBr3 heterostructures enabling tunable synapse behaviors in a two-terminal optoelectronic device

Solution-processable semiconductor heterostructures enable scalable fabrication of high performance electronic and optoelectronic devices with tunable functions via heterointerface control. In particular, artificial optical synapses require interface manipulation for nonlinear signal processing. However, the limited combinations of materials for heterostructure construction have restricted the tunability of synaptic behaviors with simple device configurations. Herein, MAPbBr3 nanocrystals were hybridized with MgAl layered double hydroxide (LDH) nanoplates through a room temperature self-assembly process. The formation of such heterostructures, which exhibited an epitaxial relationship, enabled effective hole transfer from MAPbBr3 to LDH, and greatly reduced the defect states in MAPbBr3. Importantly, the ion-conductive nature of LDH and its ability to form a charged surface layer even under low humidity conditions allowed it to attract and trap holes from MAPbBr3. This imparted tunable synaptic behaviors and short-term plasticity (STP) to long-term plasticity (LTP) transition to a two-terminal device based on the LDH-MAPbBr3 heterostructures. The further neuromorphic computing simulation under varying humidity conditions showcased their potential in learning and recognition tasks under ambient conditions. Our work presents a new type of epitaxial heterostructure comprising metal halide perovskites and layered ion-conductive materials, and provides a new way of realizing charge-trapping induced synaptic behaviors.

Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves

The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.

Azobenzene-based optoelectronic transistors for neurohybrid building blocks

Exploiting the light-matter interplay to realize advanced light responsive multimodal platforms is an emerging strategy to engineer bioinspired systems such as optoelectronic synaptic devices. However, existing neuroinspired optoelectronic devices rely on complex processing of hybrid materials which often do not exhibit the required features for biological interfacing such as biocompatibility and low Young's modulus. Recently, organic photoelectrochemical transistors (OPECTs) have paved the way towards multimodal devices that can better couple to biological systems benefiting from the characteristics of conjugated polymers. Neurohybrid OPECTs can be designed to optimally interface neuronal systems while resembling typical plasticity-driven processes to create more sophisticated integrated architectures between neuron and neuromorphic ends. Here, an innovative photo-switchable PEDOT:PSS was synthesized and successfully integrated into an OPECT. The OPECT device uses an azobenzene-based organic neuro-hybrid building block to mimic the retina's structure exhibiting the capability to emulate visual pathways. Moreover, dually operating the device with opto- and electrical functions, a light-dependent conditioning and extinction processes were achieved faithful mimicking synaptic neural functions such as short- and long-term plasticity.

Optogenetics-Inspired Fluorescent Synaptic Devices with Nonvolatility

Given the synergy of optogenetics and bioimaging in neuroscience, it is possible for light to simultaneously modulate and visualize synaptic events of optoelectronic synaptic devices, which are building blocks of a neuromorphic computing system with optoelectronic integration. Here we demonstrate the realization of the simultaneous modulation and visualization of synaptic events by using optically stimulated synaptic devices based on the heterostructure of fluorescent silicon quantum dots (Si QDs) and monolayer molybdenum disulfide (MoS₂). The charge-transfer-enabled photogating effect of the Si QDs/MoS₂ heterostructure leads to the nonvolatility of the synaptic devices, which exhibit important synaptic functionalities and synchronous fluorescence upon optical stimulation. An array of the Si QDs/MoS₂ optoelectronic synaptic devices is well-employed to mimic robust neural population coding. Defective devices in this array may be pinpointed by the absence of their fluorescence. This work has an important implication for the development of synaptic devices facilitating the system-level diagnosis and device-level positioning of a neuromorphic computing system.