Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision

Advanced Materials, Journal Year: 2023, Volume and Issue: 36(6)

Published: July 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.

Language: Английский

---

Bioinspired nanofluidic iontronics for brain-like computing

Nano Research, Journal Year: 2023, Volume and Issue: 17(2), P. 503 - 514

Published: July 14, 2023

Language: Английский

---

Online dynamical learning and sequence memory with neuromorphic nanowire networks

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Nov. 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.

Language: Английский

---

Connectome-based reservoir computing with the conn2res toolbox

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 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

Language: Английский

---

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

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(27)

Published: April 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

Language: Английский

---

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

Applied Physics Reviews, Journal Year: 2024, Volume and Issue: 11(4)

Published: Oct. 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.

Language: Английский

---

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

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 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.

Language: Английский

---

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

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 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.

Language: Английский

---

Tomography of memory engrams in self-organizing nanowire connectomes

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Sept. 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.

Language: Английский

---

Self-Assembled Monolayer and Nanoparticles Coenhanced Fragmented Silver Nanowire Network Memristor

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: 16(5), P. 6057 - 6067

Published: Jan. 29, 2024

Silver nanowire (AgNW) networks with self-assembled structures and synaptic connectivity have been recently reported for constructing neuromorphic memristors. However, resistive switching at the cross-point junctions of network is unstable due to locally enhanced Joule heating Gibbs-Thomson effect, which poses an obstacle integration threshold memory function in same AgNW memristor. Here, fragmented combined Ag nanoparticles (AgNPs) mercapto monolayers (SAMs) are devised construct memristors stable behavior. In above design, planar gaps between NW segments switching, AgNPs act as metal islands reduce voltage (

Language: Английский

---