Adaptive Decoding via Hierarchical Neural Information Gradients in Mouse Visual Tasks

2025-10-10

AI Summary

This study addresses the neglect of dynamic, cross-regional neural generation processes in current brain–vision decoding. We propose the Adaptive Topological Vision Transformer (AT-ViT). Methodologically, leveraging the Allen Neuropixel dataset, AT-ViT integrates hierarchical intra-regional neural representations with dynamic inter-regional information gradient modeling—enabling, for the first time, quantitative characterization of hierarchical information flow across visual brain regions. Crucially, we innovatively incorporate hippocampal neural stochasticity as a decoding feature and jointly employ deep neural networks with neuroinformation gradient analysis to unify fine-grained and coarse-grained decoding paradigms. Experiments demonstrate that regional information hierarchy is strongly correlated with decoding performance; visual stimulus reconstruction accuracy improves significantly; and robust, interpretable decoding is achieved across multiple tasks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Representation Learning for Vision
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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 complex data. However, most methods often overlook the dynamic generation process of neural data, such as hierarchical brain's visual data, within the brain's structure. In the decoding of brain's visual data, two main paradigms are 'fine-grained decoding tests' and 'rough-grained decoding tests', which we define as focusing on a single brain region and studying the overall structure across multiple brain regions, respectively. In this paper, we mainly use the Visual Coding Neuropixel dataset from the Allen Brain Institute, and the hierarchical information extracted from some single brain regions (i.e., fine-grained decoding tests) is provided to the proposed method for studying the adaptive topological decoding between brain regions, called the Adaptive Topological Vision Transformer, or AT-ViT. In numerous experiments, the results reveal the importance of the proposed method in hierarchical networks in the visual tasks, and also validate the hypothesis that "the hierarchical information content in brain regions of the visual system can be quantified by decoding outcomes to reflect an information hierarchy." Among them, we found that neural data collected in the hippocampus can have a random decoding performance, and this negative impact on performance still holds significant scientific value.

Problem

• Decoding dynamic neural responses to visual stimuli
• Studying hierarchical information transfer across brain regions
• Quantifying visual system information hierarchy through decoding outcomes

Innovation

• Adaptive Topological Vision Transformer for brain decoding
• Hierarchical neural information gradients in visual tasks
• Quantifying information hierarchy via decoding outcomes