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Found 17 papers, 3 papers with code
Brain-Inspired Exploration of Functional Networks and Key Neurons in Large Language Models
We use methods similar to those in the field of functional neuroimaging analysis to locate and identify functional networks in LLM.
LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models
LinBridge posits that the nonlinear mapping between ANN representations and neural responses can be factorized into a linear inherent component that approximates the complex nonlinear relationship, and a mapping bias that captures sample-selective nonlinearity.
Brain-like Functional Organization within Large Language Models
In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs), the foundational organizational structure of the human brain.
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
-Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis.
Brain Dialogue Interface (BDI): A User-Friendly fMRI Model for Interactive Brain Decoding
To address this issue, this study introduces a user-friendly decoding model that enables dynamic communication with the brain, as opposed to the static decoding approaches utilized by traditional studies.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
When Brain-inspired AI Meets AGI
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.
Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain
Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models.
BI AVAN: Brain inspired Adversarial Visual Attention Network
Visual attention is a fundamental mechanism in the human brain, and it inspires the design of attention mechanisms in deep neural networks.
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations
However, whether there exists semantic correlations/connections between the visual representations in ANNs and those in BNNs remains largely unexplored due to both the lack of an effective tool to link and couple two different domains, and the lack of a general and effective framework of representing the visual semantics in BNNs such as human functional brain networks (FBNs).
Rectify ViT Shortcut Learning by Visual Saliency
In this work, we propose a novel and effective saliency-guided vision transformer (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data.
A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers
The key characteristic of these ViT models is to adopt different aggregation strategies of spatial patch information within the artificial neural networks (ANNs).
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention
More importantly, our proposed hybrid attention modules (SA and CA) do not enforce assumptions of linearity and independence as previous methods, and thus provide a novel approach to better understanding dynamic functional brain networks.
Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles
Our strategy is to formulate the individual ROI optimization as a group variance minimization problem, in which group-wise functional and structural connectivity patterns, and anatomic profiles are defined as optimization constraints.
