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10 papers · ranked by Valyu relevance

01
BbioRxiv8 Sep 2026
Biologically grounded locality priors close the data gap for vision transformers in neural prediction
Matteo Farina, Pietro Zamberlan, Arno Onken, Ulisse Ferrari
For datasets with thousands of neurons and images, vision transformers have proven successful at predicting neural responses to stimuli. However, they are expected to underperform in low-data regimes, where CNNs and Gaussian processes are considered more effective. We ask whether transformers can be made competitive…
74relevance
02
PPubMed7 Sep 2026
ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions
Yiming Xue, Xiaojian Liu, Weimin Zhu, Shengfan Wang + 2 more
While protein-RNA interactions are fundamental to post-transcriptional processes, achieving a holistic understanding of their regulatory logic remains challenging. Current computational models often treat binding affinity, interface mapping, and RNA design as isolated tasks, thereby failing to provide a unified…
68relevance
03
BbioRxiv8 Sep 2026
PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models
Eliezer Masliah
How transient neural representations become integrated and stable enough to function as internal neural models remains incompletely understood. Grounded in efficient coding, Bayesian and predictive frameworks, recurrent and attractor dynamics, neural state-space models, and systems neuroscience, the Principle of…
68relevance
04
BbioRxiv6 Sep 2026
HyperSketch: *de Bruijn* graph sketching for genomic similarity estimation with Hyperdimensional Computing
Fabio Cumbo, Kabir Dhillon, M. Hassan Najafi, Sercan Aygun + 1 more
The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch…
62relevance
05
BbioRxiv8 Sep 2026
Deep-layer cortical tracking abruptly collapses in the absence of language comprehension
ChenTianyi Yang, Andrew Thwaites, Cai Wingfield, Chao Zhang + 1 more
The brain builds meaning from speech in stages, transforming acoustic input into linguistic comprehension. Yet where comprehension separates from general acoustic processing has been difficult to localize, because the two are tightly entangled in continuous speech. Here we align the activity of 145,000 individual…
62relevance
06
BbioRxiv7 Sep 2026
BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era
Amélie Barozet, Vincent Cabeli, Jean Ogier du Terrail, Alexey Rukhovich + 6 more
The development of climate-resilient crops would be greatly accelerated by models able to reason directly over plant genomic sequences and to pinpoint trait-associated regions or loci. Anticipating the impact of DNA base changes (variants) remains challenging, and understanding regulatory mechanisms is still an active…
61relevance
07
BbioRxiv8 Sep 2026
Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces
Yi Ren, Zimei Chen, Hayden Deans, Ying Nian Wu + 1 more
Extensive studies suggest the brain performs Bayesian inference to infer the latent world states. It is a fundamental neuroscience question that how canonical recurrent neural circuits in the brain implement Bayesian inference. Many existing theoretical studies focused on how the recurrent circuits compute the…
59relevance
08
PPubMed7 Sep 2026
Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data
Xiaoyue Hu, Yuhao Ma, Ruixing Ming, Heping Zhang + 1 more
Identifying essential biomarkers remains a core challenge in elucidating the pathogenic mechanisms and achieving precise diagnosis of complex diseases. Deep neural networks offer immense predictive power, yet their lack of interpretability severely limits downstream biological insight. Here, we introduce DeepVaris, an…
56relevance
09
PPubMed6 Sep 2026
Autistic and non-autistic children’s perceptual decision-making in visual orientation and motion tasks and the effect of task instructions
Lou Thomas, Gaia Scerif, Hodo Yusuf, Mackenzie Laird + 3 more
Diffusion decision models (DDMs) offer the potential to go beyond standard accuracy and response-time indices to better understand perceptual decision-making in autism. One unanswered question is whether autistic participants can flexibly adjust the speed and accuracy of their decision-making according to task demands.…
56relevance
10
PPubMed7 Sep 2026
Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques
Sahar Alkhaibari, Feng Dong
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in social communication, social interaction, and repetitive behaviors. Early and accurate diagnosis is essential but is often hindered by subjective clinical assessments, limited data availability, and…
50relevance

Results for encoding models

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions

PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models

HyperSketch: de Bruijn graph sketching for genomic similarity estimation with Hyperdimensional Computing

Deep-layer cortical tracking abruptly collapses in the absence of language comprehension

BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data

Autistic and non-autistic children’s perceptual decision-making in visual orientation and motion tasks and the effect of task instructions

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions

PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models

HyperSketch: de Bruijn graph sketching for genomic similarity estimation with Hyperdimensional Computing

Deep-layer cortical tracking abruptly collapses in the absence of language comprehension

BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data

Autistic and non-autistic children’s perceptual decision-making in visual orientation and motion tasks and the effect of task instructions

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Results for encoding models

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions

PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models

HyperSketch: *de Bruijn* graph sketching for genomic similarity estimation with Hyperdimensional Computing

Deep-layer cortical tracking abruptly collapses in the absence of language comprehension

BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data

Autistic and non-autistic children’s perceptual decision-making in visual orientation and motion tasks and the effect of task instructions

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

ProRB: a structure-free unified framework for joint prediction and design of protein–RNA interactions

PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models

HyperSketch: *de Bruijn* graph sketching for genomic similarity estimation with Hyperdimensional Computing

Deep-layer cortical tracking abruptly collapses in the absence of language comprehension

BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data

Autistic and non-autistic children’s perceptual decision-making in visual orientation and motion tasks and the effect of task instructions

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

HyperSketch: de Bruijn graph sketching for genomic similarity estimation with Hyperdimensional Computing

HyperSketch: de Bruijn graph sketching for genomic similarity estimation with Hyperdimensional Computing