Bioinspired nanofluidic iontronics for brain-like computing

Nano Research, Год журнала: 2023, Номер 17(2), С. 503 - 514

Опубликована: Июль 14, 2023

Язык: Английский

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Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision

Advanced Materials, Год журнала: 2023, Номер 36(6)

Опубликована: Июль 12, 2023

Abstract The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal‐oxide semiconductor (CMOS) circuits owing its high latency and inefficient power consumption originating from the data shuffling between memory computation units. Gaining more insights into function every part visual pathway for perception can bring capabilities in terms robustness generality. Hardware acceleration energy‐efficient biorealistic highly necessitates neuromorphic devices that are able mimic each pathway. In this paper, we review structure entire class neurons retina primate cortex within reach (Chapter 2) reviewed. Based extraction biological principles, recent hardware‐implemented located different parts discussed detail Chapters 3 4. Furthermore, valuable applications inspired scenarios 5) provided. functional description devices/circuits expected provide design next‐generation systems.

Язык: Английский

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Online dynamical learning and sequence memory with neuromorphic nanowire networks

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Ноя. 1, 2023

Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties nanostructured materials. In addition their neural network-like structure, NWNs also exhibit resistive memory switching in response electrical inputs due synapse-like changes conductance at nanowire-nanowire cross-point junctions. Previous studies have demonstrated how dynamics generated by can be harnessed for temporal learning tasks. This study extends these findings further demonstrating online from spatiotemporal dynamical features using image classification and sequence recall tasks implemented on NWN device. Applied MNIST handwritten digit task, with device achieves overall accuracy 93.4%. Additionally, we find a correlation between individual classes mutual information. The task reveals patterns embedded enable pattern. Overall, results provide proof-of-concept elucidate enhance learning.

Язык: Английский

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Connectome-based reservoir computing with the conn2res toolbox

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Янв. 22, 2024

Abstract The connection patterns of neural circuits form a complex network. How signaling in these manifests as cognition and adaptive behaviour remains the central question neuroscience. Concomitant advances connectomics artificial intelligence open fundamentally new opportunities to understand how shape computational capacity biological brain networks. Reservoir computing is versatile paradigm that uses high-dimensional, nonlinear dynamical systems perform computations approximate cognitive functions. Here we present : an open-source Python toolbox for implementing networks modular, allowing arbitrary network architecture dynamics be imposed. allows researchers input connectomes reconstructed using multiple techniques, from tract tracing noninvasive diffusion imaging, impose systems, spiking neurons memristive dynamics. versatility us ask questions at confluence neuroscience intelligence. By reconceptualizing function computation, sets stage more mechanistic understanding structure-function relationships

Язык: Английский

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Li Promoting Long Afterglow Organic Light‐Emitting Transistor for Memory Optocoupler Module

Advanced Materials, Год журнала: 2024, Номер 36(27)

Опубликована: Апрель 15, 2024

The artificial brain is conceived as advanced intelligence technology, capable to emulate in-memory processes occurring in the human by integrating synaptic devices. Within this context, improving functionality of transistors increase information processing density neuromorphic chips a major challenge field. In article, Li-ion migration promoting long afterglow organic light-emitting transistors, which display exceptional postsynaptic brightness 7000 cd m

Язык: Английский

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Recent trends in neuromorphic systems for non-von Neumann in materia computing and cognitive functionalities

Applied Physics Reviews, Год журнала: 2024, Номер 11(4)

Опубликована: Окт. 1, 2024

In the era of artificial intelligence and smart automated systems, quest for efficient data processing has driven exploration into neuromorphic aiming to replicate brain functionality complex cognitive actions. This review assesses, based on recent literature, challenges progress in developing basic focusing “material-neuron” concepts, that integrate structural similarities, analog memory, retention, Hebbian learning brain, contrasting with conventional von Neumann architecture spiking circuits. We categorize these devices filamentary non-filamentary types, highlighting their ability mimic synaptic plasticity through external stimuli manipulation. Additionally, we emphasize importance heterogeneous neural content support conductance linearity, plasticity, volatility, enabling effective storage various types information. Our comprehensive approach categorizes fundamentally different under a generalized pattern dictated by driving parameters, namely, pulse number, amplitude, duration, interval, as well current compliance employed contain conducting pathways. also discuss hybridization protocols fabricating systems making use existing complementary metal oxide semiconductor technologies being practiced silicon foundries, which perhaps ensures smooth translation user interfacing new generation devices. The concludes outlining insights challenges, future directions realizing deployable field intelligence.

Язык: Английский

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Ephaptic Coupling in Ultralow‐Power Ion‐Gel Nanofiber Artificial Synapses for Enhanced Working Memory

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Март 10, 2025

Neuromorphic devices are designed to replicate the energy-efficient information processing advantages found in biological neural networks by emulating working mechanisms of neurons and synapses. However, most existing neuromorphic focus primarily on functionally mimicking synapses, with insufficient emphasis ion transport mechanisms. This limitation makes it challenging achieve complexity connectivity inherent systems, such as ephaptic coupling. Here, an ionic biomimetic synaptic device based a flexible ion-gel nanofiber network is proposed, which transmits enables coupling through capacitance formation extremely low energy consumption just 6 femtojoules. The hysteretic behavior endows synaptic-like memory effects, significantly enhancing performance reservoir computing system for classifying MNIST handwritten digit dataset demonstrating high efficiency edge learning. More importantly, array establish communication connections, exhibiting global oscillatory behaviors similar networks. perform tasks, paving way developing brain-like systems characterized vast connectivity.

Язык: Английский

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Self‐organized Criticality in Neuromorphic Nanowire Networks With Tunable and Local Dynamics

Advanced Functional Materials, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Abstract Self‐organized criticality (SOC) has attracted large interest as a key property for the optimization of information processing in biological neural systems. Inspired by this synergy, nanoscale self‐organizing devices are demonstrated to emulate critical dynamics due their complex nature, proving be ideal candidates hardware implementation brain‐inspired unconventional computing paradigms. However, controlling emerging and understanding its relationship with capabilities remains challenge. Here, it is shown that memristive nanowire networks (NWNs) can programmed state through appropriate electrical stimulation. Furthermore, multiterminal characterization reveals network areas establish spatial interactions endowing local dynamics. The impact such tunable versus experimentally analyzed materia nonlinear transformation (NLT) tasks, framework reservoir computing. As brain where cortical specialized certain function, performance rely on response reduced subsets outputs, which may show or not, depending specificity task. Such brain‐like behavior lead neuromorphic systems based complexity exploiting behavior.

Язык: Английский

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SiC@NiO Core–Shell Nanowire Networks‐Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature

Small, Год журнала: 2024, Номер 20(34)

Опубликована: Апрель 12, 2024

Abstract 1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural have emerged as a promising platform for neuromorphic systems. Based on brain‐like structures synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high‐efficient sensing computing function high processing rates low power consumption. Here, high‐temperature based SiC@NiO core–shell networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain short/long‐term plasticity modulation under both electrical optical stimulation, exhibit advanced functions such memory “learning–forgetting–relearning” stimulation at room temperature 200 °C. light stimulus, constructed 5 × 3 array stable visual up to °C, which be utilized develop artificial Additionally, when exposed multiple electronic or stimuli, effectively replicate principles Pavlovian classical conditioning, heterologous functionality refining networks. Overall, abundant characteristics thermal stability, these offer route advancing

Язык: Английский

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Tomography of memory engrams in self-organizing nanowire connectomes

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Сен. 27, 2023

Self-organizing memristive nanowire connectomes have been exploited for physical (in materia) implementation of brain-inspired computing paradigms. Despite having shown that the emergent behavior relies on weight plasticity at single junction/synapse level and wiring involving topological changes, a shift to multiterminal paradigms is needed unveil dynamics network level. Here, we report tomographical evidence memory engrams (or traces) in connectomes, i.e., physicochemical changes biological neural substrates supposed endow representation experience stored brain. An experimental/modeling approach shows spatially correlated short-term effects can turn into long-lasting engram patterns inherently related topology inhomogeneities. The ability exploit both encoding consolidation information same substrate would open radically new perspectives materia computing, while offering neuroscientists an alternative platform understand role learning knowledge.

Язык: Английский

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