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Neuromorphic Computing and Engineering
Scopus
ISSN 2634-4386 · IOP Publishing · 293 artikel di indeks
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Artikel Jurnal
ATW-SNN: low-accuracy-loss asymmetric ternary-weight spiking neural network with spike calibration strategy
The deployment of spiking neural networks (SNNs) on resource-constrained devices is mainly limited by memory footprint and energy consumption. Specifically, these limitations stem from the overhead incurred by full-precision storage and floating-point operations. To address these bottlenecks, we…
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Artikel Jurnal
Memristor-based spiking neuron circuit design for address-event representation edge motion tracking
Address-event representation (AER)-based imaging offers high-speed, high-dynamic-range vision by processing the pixel-level visual signals in the form of spatiotemporal, asynchronous spikes. The conventional AER systems rely on complex CMOS silicon spiking neuron circuits and can suffer from dense…
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Artikel Jurnal
A scalable hybrid training approach for recurrent spiking neural networks
Recurrent spiking neural networks (RSNNs) can be implemented very efficiently in neuromorphic systems. Nevertheless, training of these models with powerful gradient-based learning algorithms is mostly performed on standard digital hardware using backpropagation through time (BPTT). However, BPTT…
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Artikel Jurnal
A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks
The majority of research in both training artificial neural networks (ANNs) and modeling learning in biological brains focuses on synaptic plasticity, where learning equates to changing the strength of existing connections. However, in biological brains, structural plasticity—where new connections…
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Artikel Jurnal
Symbol detection in a MIMO wireless communication system using a FeFET-coupled CMOS ring oscillator array
Symbol decoding in multiple-input multiple-output (MIMO) wireless communication systems requires the deployment of fast, energy-efficient computing hardware deployable at the edge. The brute-force and exact maximum likelihood (ML) decoder, solved on conventional classical digital hardware to decode…
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Artikel Jurnal
Toward a multiscale theoretical framework for organic memristive materials
Neuromorphic computing aspires to overcome the intrinsic inefficiencies of von Neumann architectures by co-locating memory and computation in physical devices that emulate biological neurons and synapses. Memristive materials stand at the core of this paradigm, enabling non-volatile…
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Artikel Jurnal
Dynamical alignment: a principle for adaptive neural computation
Computational capabilities are typically viewed as fixed by static network architecture. We establish ‘dynamical alignment’: a principle demonstrating that a fixed computational substrate can be steered into fundamentally different operational modes solely by controlling its inputs’ temporal…
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Artikel Jurnal
Reservoir computing with a heterogeneous distribution of ionic nanofluidic memristors
Nanofluidic memristive systems exhibit the nonlinear behavior and the short-time plasticity needed for reservoir computing (RC) networks. They use ions as information carriers and operate in an electrochemical environment, in resemblance to the biological synapses. Here we present simulation…
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Artikel Jurnal
Spiking transformer with learnable threshold mechanism for underwater image dehazing to aid vision-based navigation
In this paper, we introduce snnTrans-DHZ, a lightweight spiking transformer architecture that incorporates a learnable threshold membrane potential mechanism for underwater image dehazing. Despite its compact design with 0.57 M parameters, the method substantially improves underwater image clarity…
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Artikel Jurnal
Efficient transformer adaptation for analog in-memory computing via low-rank adapters
Analog in-memory computing (AIMC) offers a promising solution to the von Neumann bottleneck. However, deploying transformer models on AIMC remains challenging due to their inherent need for flexibility and adaptability across diverse tasks. For the benefits of AIMC to be fully realized, weights of…
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Artikel Jurnal
Closed-loop control of seizure activity via real-time seizure forecasting by reservoir neuromorphic computing
Closed-loop brain stimulation holds potential as personalized treatment for drug-resistant epilepsy (DRE) but still suffers from limitations that result in highly variable efficacy. First, stimulation is typically delivered upon detection of the seizure to abort rather than prevent it; second, the…
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Artikel Jurnal
Beyond rate coding: surrogate gradients enable spike timing learning in spiking neural networks
The surrogate gradient descent (SGD) algorithm enabled spiking neural networks (SNNs) to be trained to carry out challenging sensory processing tasks, an important step in understanding how spikes contribute to neural computations. However, it is unclear the extent to which these algorithms fully…
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Artikel Jurnal
Mimicking cochlear pre-processing using critically coupled MEMS sensors
The characteristic of our hearing is essentially based on the mechanics in our inner ear. Around 3000 hair cells in the cochlea decode vibrations into electrical signals, covering frequencies from 0.020–20 kHz with relative resolutions normalized by their natural frequency of 0.1%–0.4% and a high…
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Artikel Jurnal
Constructive community race: full-density spiking neural network model drives neuromorphic computing
The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below…
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Artikel Jurnal
Spatiotemporal radar gesture recognition with hybrid spiking neural networks: balancing accuracy and efficiency
Radar-based human activity recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the first application of spiking neural networks (SNNs) for radar-based HAR on aircraft marshaling signal classification. Our…
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Artikel Jurnal
A neuromorphic architecture for scalable event-based control
This paper introduces the ‘rebound winner-take-all (RWTA)’ motif as the basic element of a scalable neuromorphic control architecture. From the cellular level to the system level, the resulting architecture combines the reliability of discrete computation and the tunability of continuous…
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Artikel Jurnal
Neuromorphic hardware based on memristive nanodevices for seizure detection and recovery
During the last decades, neuromorphic engineers have developed specific hardware designed to build efficient computing systems inspired by the structure of the human brain. The emergence of nanoscale memristors provided these systems with a new component which can approximately emulate the behavior…
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Artikel Jurnal
RCbench: a unified framework for benchmarking reservoir computing systems
Reservoir computing (RC) is a computational framework where a fixed dynamical reservoir projects an input into a higher-dimensional state that is then analyzed by a readout , which is trained to map the reservoir state into the desired output. While the conventional RC paradigm is based on dynamics…
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Artikel Jurnal
GPU and RISC-V acceleration for neuromorphic computing based on spiking neural networks: taxonomy, comparison, and open challenges
Neuromorphic computing has emerged as an event-driven, energy-efficient paradigm for brain-like information processing. Unlike conventional architectures, it unifies memory and computation to mitigate the von Neumann bottleneck, and it typically relies on spiking neural networks (SNNs) as its…
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Artikel Jurnal
Neuromorphic imaging cytometry: event-driven cell analytics powered by AI
Imaging flow cytometry (IFC) provides spatial resolution and multi-parametric measurements to characterise cells of interest. It enables profound insights into cell signalling, co-localisation, cell-to-cell interaction, and DNA studies. However, the use of traditional frame-based sensors (FBS)…