数据来源：ArXiv Domain LLM Domain Papers1. On sources to variabilities of simple cells in the primary visual cortex: A principled theory for the interaction between geometric image transformations and receptive field responsesThis paper gives an overview of a theory for modelling the interaction between geometric image transformations and receptive field responses for a visual observer that views objects and spatio-temporal events in the environment. This treatment is developed over combinations of ...

数据来源：ArXiv Domain LLM Domain Papers1. REVE: A Foundation Model for EEG — Adapting to Any Setup with Large-Scale Pretraining on 25,000 SubjectsFoundation models have transformed AI by reducing reliance on task-specific data through large-scale pretraining. While successful in language and vision, their adoption in EEG has lagged due to the heterogeneity of public datasets, which are collected under varying protocols, devices, and electrode configurations. Existing EEG foundation models struggle ...

数据来源：ArXiv Domain LLM Domain Papers1. Transformer brain encoders explain human high-level visual responsesA major goal of neuroscience is to understand brain computations during visual processing in naturalistic settings. A dominant approach is to use image-computable deep neural networks trained with different task objectives as a basis for linear encoding models. However, in addition to requiring estimation of a large number of linear encoding parameters, this approach ignores the structure ...

数据来源：ArXiv Domain LLM Domain Papers1. Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?Object binding, the brain’s ability to bind the many features that collectively represent an object into a coherent whole, is central to human cognition. It groups low-level perceptual features into high-level object representations, stores those objects efficiently and compositionally in memory, and supports human reasoning about individual object instances. While prior work ofte ...

数据来源：ArXiv Domain LLM Domain Papers1. Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?Object binding, the brain’s ability to bind the many features that collectively represent an object into a coherent whole, is central to human cognition. It groups low-level perceptual features into high-level object representations, stores those objects efficiently and compositionally in memory, and supports human reasoning about individual object instances. While prior work ofte ...

数据来源：ArXiv Domain LLM Domain Papers1. Brain-IT: Image Reconstruction from fMRI via Brain-Interaction TransformerReconstructing images seen by people from their fMRI brain recordings provides a non-invasive window into the human brain. Despite recent progress enabled by diffusion models, current methods often lack faithfulness to the actual seen images. We present “Brain-IT”, a brain-inspired approach that addresses this challenge through a Brain Interaction Transformer (BIT), allowing effectiv ...

数据来源：ArXiv Domain LLM Domain Papers1. Brain-IT: Image Reconstruction from fMRI via Brain-Interaction TransformerReconstructing images seen by people from their fMRI brain recordings provides a non-invasive window into the human brain. Despite recent progress enabled by diffusion models, current methods often lack faithfulness to the actual seen images. We present “Brain-IT”, a brain-inspired approach that addresses this challenge through a Brain Interaction Transformer (BIT), allowing effectiv ...

数据来源：ArXiv Domain LLM Domain Papers1. A Sensing Whole Brain Zebrafish Foundation Model for Neuron Dynamics and BehaviorNeural dynamics underlie behaviors from memory to sleep, yet identifying mechanisms for higher-order phenomena (e.g., social interaction) is experimentally challenging. Existing whole-brain models often fail to scale to single-neuron resolution, omit behavioral readouts, or rely on PCA/conv pipelines that miss long-range, non-linear interactions. We introduce a sparse-attentio ...

数据来源：ArXiv Domain LLM Domain Papers1. Brain-IT: Image Reconstruction from fMRI via Brain-Interaction TransformerReconstructing images seen by people from their fMRI brain recordings provides a non-invasive window into the human brain. Despite recent progress enabled by diffusion models, current methods often lack faithfulness to the actual seen images. We present “Brain-IT”, a brain-inspired approach that addresses this challenge through a Brain Interaction Transformer (BIT), allowing effectiv ...

数据来源：ArXiv Domain LLM Domain Papers1. A Sensing Whole Brain Zebrafish Foundation Model for Neuron Dynamics and BehaviorNeural dynamics underlie behaviors from memory to sleep, yet identifying mechanisms for higher-order phenomena (e.g., social interaction) is experimentally challenging. Existing whole-brain models often fail to scale to single-neuron resolution, omit behavioral readouts, or rely on PCA/conv pipelines that miss long-range, non-linear interactions. We introduce a sparse-attentio ...

数据来源：ArXiv Domain LLM Domain Papers1. The Physical Basis of Prediction: World Model Formation in Neural Organoids via an LLM-Generated CurriculumThe capacity of an embodied agent to understand, predict, and interact with its environment is fundamentally contingent on an internal world model. This paper introduces a novel framework for investigating the formation and adaptation of such world models within a biological substrate: human neural organoids. We present a curriculum of three scalable, ...

数据来源：ArXiv Domain LLM Domain Papers1. Fast weight programming and linear transformers: from machine learning to neurobiologyRecent advances in artificial neural networks for machine learning, and language modeling in particular, have established a family of recurrent neural network (RNN) architectures that, unlike conventional RNNs with vector-form hidden states, use two-dimensional (2D) matrix-form hidden states. Such 2D-state RNNs, known as Fast Weight Programmers (FWPs), can be interpreted ...

数据来源：ArXiv Domain LLM Domain Papers1. CBraMod: A Criss-Cross Brain Foundation Model for EEG DecodingElectroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the success of large language models, there is a growing body of studies focusing on ...

数据来源：ArXiv Domain LLM Domain Papers1. CBraMod: A Criss-Cross Brain Foundation Model for EEG DecodingElectroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the success of large language models, there is a growing body of studies focusing on ...

数据来源：ArXiv Domain LLM Domain Papers1. CBraMod: A Criss-Cross Brain Foundation Model for EEG DecodingElectroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, limited by specific tasks and datasets, hindering model performance and generalizability. With the success of large language models, there is a growing body of studies focusing on ...