26 papers · ranked by Valyu relevance
Hong-Yun Ou, Takahiro Hasegawa, Osamu Fukayama, Eizo Miyashita
Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their…
Kai Zhang, Zhengzhong Yi, Shaojun Guo, Linghang Kong + 12 more
Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation. Neural network decoders like AlphaQubit have demonstrated significant potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders…
Andrew Garrett Kurbis, Alex Mihailidis, Brokoslaw Laschowski
Decoding algorithms can be used to predict motor behaviour from patterns of neural activity. However, most studies rely on subject-optimized models, limiting generalization and scalability to novel subjects and tasks. Building on recent advances in deep learning and large-scale data, here we developed an EMG foundation…
Mohaimen Mohammed, Mesut Çevik, Stefano Savazzi
This paper presents a Deep Autoencoder-LDPC-OFDM (DAE-LDPC-OFDM) transceiver architecture that integrates a learned belief propagation (BP) decoder to achieve robust, energy-efficient, and adaptive wireless communication. Unlike conventional modular systems that treat encoding, modulation, and decoding as independent…
Rostislav Gusev, Nikita Aleksandrov, Artem Solomkin, Dmitry Artemasov
Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown strong performance for short codes, but their scalability to longer codes is limited by the quadratic memory and computational cost of attention. In this paper, we introduce the…
Joseph Soo, Kam Pang So, Xin Tang
The most accurate neural decoder on held-out trials is not necessarily the most useful for brain-computer interfaces or neural population analysis. In practical use, neural decoders may also need to remain robust to noisy neural inputs, satisfy calibration or deployment constraints, and produce comparable…
Cecilia Rossi, Marko Bumbasirevic, Paul Čvančara, Thomas Stieglitz + 3 more
Limb loss causes severe sensorimotor deficits and often necessitates prosthetic devices, particularly in lower-limb amputees. Although direct neural recording from residual nerves offers a biomimetic route for prosthetic control, low signal amplitudes and challenges in nerve interfacing have limited adoption.…
Xin-Ya Zhang, Hang Lin, Zeyu Deng, Markus Siegel + 2 more
Artificial neural networks (ANNs) offer a data-driven approach to reveal brain regional functions without explicit supervision. Here, we demonstrate that an ANN trained to decode visual stimuli from multi-unit spiking activity in monkeys, can not only reconstruct complex and dynamic scenes, but also spontaneously align…
G. Bilodeau, A. Miao, G. Gagnon-Turcotte, C. Ethier + 1 more
Bidirectional interfaces combined with neural de-coding algorithms are essential for closed-loop (CL) neuromodulation, enabling simultaneous neural monitoring and responsive optogenetic stimulation. However, implementing these capabilities in compact wireless headstages for freely moving animals remains challenging, as…
Youkun Qian, Changjiang Liu, Peixi Yu, Xingchen Ran + 12 more
Speech brain-computer interfaces (BCIs) offer a promising means to provide functional communication capacity for patients with anarthria caused by neurological conditions such as amyotrophic lateral sclerosis (ALS) or brainstem stroke. Current speech decoding research has predominantly focused on English using…
Park, Seong-Joon, Kwak, Hee-Youl + 2 more
> For reliable large-scale quantum computation, a quantum error correction (QEC) scheme must effectively resolve physical errors to protect logical information. Leveraging recent advances in deep learning, neural network-based decoders have emerged as a promising approach to enhance the reliability of QEC. We propose…
Haoyu Lei, Mohammad Jalali, Chin Wa Lau, Farzan Farnia
Recent advances in deep learning have led to AI-based error correction decoders that report empirical performance improvements over traditional belief-propagation (BP) decoding on AWGN channels. While such gains are promising, a fundamental question remains: where do these improvements come from, and what cost is paid…
Ahmed El-Gazzar, Marcel van Gerven
The rapid growth of large-scale neuroscience datasets has spurred diverse modeling strategies, ranging from mechanistic models grounded in biophysics, to phenomenological descriptions of neural dynamics, to data-driven deep neural networks (DNNs). Each approach offers distinct strengths as mechanistic models provide…
Yuhang Wang, Weihua Chen, Linjing Song, Zhiping Xu + 6 more
With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high-efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two-stage framework with high computational complexity and frequently struggle to…
Alon Helvits, Eliya Nachmani
Error-correcting codes enable reliable communication, yet practical soft decoding remains challenging across code families and block lengths. We propose SB-ECC, a score-based decoder that casts decoding as continuous-time denoising. A neural denoiser defines a probability-flow ordinary differential equation (ODE) that…
María Peña Fernández, Lara Lloret Iglesias, Jesús Marco de Lucas
One of the most compelling ideas for bridging neuroscience and artificial neural networks is the establishment of a framework based on three main components: network architecture, optimization mechanism, and loss (or objective) function to be minimized. While the first two components have been extensively explored, the…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…
Mohammadreza Khodashenas, Daniel P. Martins
Introduction Understanding how artificial neural networks (ANNs) can capture biologically meaningful dynamics is a central challenge in systems neuroscience. In this work, we investigate whether spiking neural networks (SNNs) can function not only as machine-learning tools but also as biologically inspired…
Eike-Manuel Edelmann
Künstliche Intelligenz, insbesondere das maschinelle Lernen mit künstlichen neuronalen Netzen (ANNs), ermöglicht es, moderne Kommunikationssysteme bis an ihre theoretischen Leistungsgrenzen zu führen. Diese hohe Komplexität geht jedoch mit einem steigenden Energieverbrauch einher. Gepulste neuronale Netze (engl.…
Authors not listed
Machine learning (ML) models have been widely used as efficient surrogates to predict adsorption in metal-organic frameworks (MOFs), for gas storage, chemical separations, and catalysis applications. The “black box” nature of these ML models, however, remains a significant barrier between predictions and the design of…
Ankit Gupta, Dizdar, Onur, Yun Chen + 3 more
—In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicRx), replacing the channel estimation, equalization and symbol demapping blocks. We leverage domain knowledge to design the input with spiking encoding and…
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
Authors not listed
Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present MDtrajNet-1, a foundational AI model that directly generates MD trajectories across chemical space, bypassing force calculations and…
Authors not listed
In this work, we present EquiNet, a neural network for predicting vapor–liquid equilibrium (VLE) in novel binary mixtures through direct estimation of activity coefficients and vapor pressures. The model embeds a classic excess-Gibbs free energy formulation, ensuring Gibbs–Duhem consistency on all predicted activity…
Authors not listed
We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules. AshGC is a graph neural network-based method for efficient…
Authors not listed
We present the next generation of AMP, a neural network potential (NNP) with anisotropic message passing designed to study large biomolecular systems at DFT accuracy in the condensed phase using a multiscale approach similar to quantum-mechanics/molecular-mechanics (QM/MM) with electrostatic embedding. We trained AMPv3…