26 papers · ranked by Valyu relevance
Lorenzo Posani
Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for…
Alexis D MacIntyre, Clément Gaultier, Tobias Goehring
During speech perception, properties of the acoustic stimulus can be reconstructed from the listener’s brain using methods such as electroencephalography (EEG). Most studies employ the amplitude envelope as a target for decoding; however, speech acoustics can be characterised on multiple dimensions, including as…
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…
Dmitry Patashov, Li Liu, Jion Tominaga, Kai Nakajima + 5 more
This study suggests a new analysis pipeline of MEG data, uniquely designed for neural decoding of small-sized datasets. It combines classic methods that assume stationarity of the data together with non-stationary methods to compensate for the distortions created by the classic approach. Popular Fourier-based methods…
Jan Sobotka, Luca Baroni, Ján Antolík
Decoding visual stimuli from neural population activity is crucial for understanding the brain and for applications in brain-machine interfaces. However, such biological data is often scarce, particularly in primates or humans, where highthroughput recording techniques, such as two-photon imaging, remain challenging or…
Joshua P. Chu, Michael E. Coulter, Eric L. Denovellis, Trevor Thai K. Nguyen + 5 more
Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing…
Matteo Ciferri, Matteo Ferrante, Nicola Toschi
Understanding how neural activity gives rise to perception is a central challenge in neuroscience. We address the problem of decoding visual information from highdensity intracortical recordings in primates, using the THINGS Ventral Stream Spiking Dataset. We systematically evaluate the effects of model architecture…
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…
Théo Desbordes, Itsaso Olasagasti, Nicolas Piron, Sophie Schwartz + 1 more
Multivariate decoding analyses have become a cornerstone method in cognitive neuroscience. When applied to time-resolved brain imaging signals, they provide insights into the temporal dynamics of information processing in the brain. In particular, the temporal generalization (TG) method—where a decoder trained at one…
Francisco Miqueles, Adrián G. Palacios, John Atkinson, María-José Escobar
Introduction Understanding how deep learning models map neural population activity to stimuli requires both high predictive accuracy and interpretable internal mechanisms. Methods In this work, we employ the POYO framework, a scalable transformer architecture based on spike tokenization and latent modeling, to decode…
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…
Jingyi Feng, Xiang Feng
—Understanding the encoding and decoding mechanisms of dynamic neural responses to different visual stimuli is an important topic in exploring how the brain represents visual information. Currently, hierarchically deep neural networks (DNNs) have played a significant role as tools for mining the core features of…
Hee Kyu Lee, Hyun Bin Kim, Sang Uk Park, Janghoon Joo + 6 more
Brain-computer interfaces (BCIs) have made consistent advances in supporting motor and communication functions; nevertheless, their adoption in everyday environments remains constrained by enduring challenges, including chronic instability at the electrode-tissue interface, motion-induced artifacts, inter-user…
Gautam Agarwal, Seiji Akera, Brian Lustig, Eva Pastalkova + 2 more
Local field potentials (LFPs) reflect coordination among neural populations, yet their exact relationship to neural computation remains unknown. One exception is the theta rhythm of the rodent hippocampus, which organizes sequential firing among place cells, enabling spike timing to track the animal’s path through its…
Matteo Ciferri, Matteo Ferrante, Nicola Toschi
Characterizing the information content of intracortical signals during visual processing is a central challenge in systems neuroscience. We address the problem of decoding visual information from high-density intracortical recordings in primates, using the THINGS Ventral Stream Spiking Dataset. We systematically…
Robert Worden
This paper uses simple arguments to derive a negative conclusion: that a computer cannot be conscious. If the brain is only a neural computer, brains cannot be conscious. Consciousness implies that there is something else happening in the brain, besides computation. In a running computer, information about outside…
Xing Gao, Dazhong Rong, Qinming He
Investigating the mapping between visual stimuli and neural responses in the visual cortex contributes to a deeper understanding of biological visual processing mechanisms. Most existing studies characterize this mapping by training models to directly encode visual stimuli into neural responses or decode neural…
H. Fareed Ahmed, Toktam Samiei, Erfan Nozari
Although neural activity is organized across multiple temporal and spatial scales, the principles determining information representation across scales remain unclear. In particular, while recent empirical results have reported mesoscale optimality in neural decoding, no theoretical accounts exist that can explain when…
Xin Wang, Zhuangzhi Gao, Hongyi Qin, Zhongli Wu + 2 more
Understanding the neural mechanisms underlying visual computation has long been a central challenge in neuroscience. Recent alignment based approaches have improved the accuracy of decoding visual stimuli from brain activity, yet they provide limited insight into the neural computations that give rise to these…
Johnson Zhou, Daniel Tanneberg, Forough Habibollahi, Alon Loeffler + 11 more
Biological neural networks (BNNs) have been established as a powerful and adaptive substrate that offer the potential for incredibly energy and data efficient information processing with distinct learning mechanisms. Yet a core challenge to utilizing BNN for neurocomputation is determining the optimal encoding and…
Martin Schottlender, Veronika Volkova, Pengjie Zhou, Ruifeng Zheng + 2 more
Interfacing with Biological Neural Networks (BNNs) requires encoding information into stimulation patterns that can be effectively processed and that enable the underlying system to adapt. Nevertheless, the role of stimulation encoding remains poorly understood. In this work, we compare multiple encoding strategies…
Haitao Wu, Qirui Zhang, Zhouheng Yao, Shangquan Sun + 7 more
Modeling the bidirectional correspondence between external sensory stimuli and internal neural activity has emerged as a critical frontier in neuroscience. However, existing approaches predominantly treat brain encoding and decoding as isolated tasks, relying heavily on unimodal alignment and external priors while…
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…
Authors not listed
Large Language Models have demonstrated impressive capabilities in natural language understanding and processing. However, as AI and LLMs continue to evolve, their ability to accurately and efficiently interpret data from scientific figures and plots remains obscure. In this study, we test and evaluate the ability of…
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…
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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…