Brain-computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed-window decoding that may result in redundant observation or unreliable predictions due to insufficient evidence. Adaptive temporal decision-making (ATDM) addresses this accuracy-time trade-off by progressively...
Beining Cao, Ziyi Zhao, Xiaowei Jiang et al.· 0 citations
Sign differences suggest that a single comparator may not provide an architecture-invariant decomposition of a pretrained-versus-supervised performance gap, and validation-fitted temperature scaling returns foundation-model calibration error to the supervised range despite substantially lower four-class accuracy.
A compact, reusable representation for array-wide spiking activity without learned assay-specific parameters is established, providing a scalable foundation for generative modeling across diverse neural preparations.
Md Sayed Tanveer, M. Mostajo-Radji, Wang Ge· 0 citations
BrainWideBench is presented, a benchmark for evaluating across-animal transfer on multi-region neural recordings, built on the International Brain Laboratory Brainwide Map dataset of neural and behavioral recordings spanning 276 brain regions from 139 mice performing a sensory-guided decision-making task.
Alexandre Andre, S. Mahato, Vinam Arora et al.· arXiv.org· 1 citation
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2D and 3D videos could generally induce a higher cognitive load, but the extent of the differences also depended on the contents of the video stimuli and the viewing purpose, and the Cognitive Load Index (CLI) was introduced.
Fixed points of recurrent neural networks can be leveraged to store and generate information. These fixed points are captured by the Boltzmann-Gibbs measure, which leads to neural Langevin dynamics that relax to those fixed points for generative learning of a real dataset. We call this type of generative model a neural...
A montage-agnostic cross-user encoder is carried to eleven transradial amputees on a protocol matched to its intact-limb training data, and the prediction pre-registered for this study, which extends the encoder's baseline-strength account with the premise that amputee EMG is less separable, holds true only after a few...
The model presented here is a step toward a more rigorous formalization of cognitive phenomena and may provide a basis for further theoretical analysis, computer simulation, and implementation in artificial-intelligence systems inspired by biological processes.
This study develops NeuroSketch, a practical design recipe for neural decoding, through a basic architecture study followed by macro- and micro-level optimization, and finds that CNN-2D outperforms other architectures in neural decoding tasks and explores its effectiveness from temporal and spatial perspectives.
Gao-Rui Zhang, Zhi-Zhang Yuan, Jia-Lan Yang et al.· 0 citations
Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers. While previous studies have examined their neural correlates, it remains unclear how multi-dimensional electroencephalography (EEG) features contribute to their characte...
Yuwen Zeng, Dengzhe Hou, Zhang Zhang et al.· 0 citations
It is shown that for every approximation bound that is valid for a set of multi-spike neural networks, there is an equivalent set of single-spike neural networks with only linearly more (or less) neurons, in the maximum number of spikes, for which the bound holds.
Dominik Dold, Philipp C. Petersen· arXiv.org· 2 citations
The ability to generalize to novel motor sequences within the closed-loop control setting is demonstrated, improving on the generalization error of a task-input-conditioned multitask baseline by upto order of magnitude.
Sreejan Kumar, M. Mattar, Lea Duncker· 0 citations
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