Neural State

Neural state research focuses on understanding the dynamic patterns of brain activity underlying cognition and behavior. Current efforts utilize diverse approaches, including deep learning models like variational recurrent neural networks and convolutional networks, to analyze fMRI and EEG data, often incorporating techniques like attention mechanisms and topological data analysis to extract meaningful representations of neural dynamics. This research aims to improve our understanding of brain function, leading to advancements in brain-computer interfaces, personalized medicine (e.g., using neurofeedback learning patterns for diagnosis), and the development of more sophisticated artificial intelligence systems inspired by biological neural networks.

Papers

November 4, 2024

Do graph neural network states contain graph properties?
Tom Pelletreau-Duris, Ruud van Bakel, Michael Cochez
Graph Neural Network Graph Drawing Inherent Interpretability Graph Feature Graph Learning Model Neural State Model Agnostic Explainability

October 6, 2024

BrainCodec: Neural fMRI codec for the decoding of cognitive brain states
Yuto Nishimura, Masataka Sawayama, Ayumu Yamashita, Hideki Nakayama, Kaoru Amano
Functional Magnetic Resonance Imaging Neural Topic Accurate Decoding Neural Audio Neural State Vec Tok Codec FMRI Encoding fMRI Reconstruction

October 1, 2024

TAVRNN: Temporal Attention-enhanced Variational Graph RNN Captures Neural Dynamics and Behavior
Moein Khajehnejad, Forough Habibollahi, Ahmad Khajehnejad, Brett J. Kagan, Adeel Razi
Graph Attention BEHAVIOR Explanation Temporal Attention Neuronal Network Neural State

September 19, 2024

How the (Tensor-) Brain uses Embeddings and Embodiment to Encode Senses and Decode Symbols
Volker Tresp, Hang Li
Jina Embeddings Brain Function Accurate Decoding Semantic Memory Different Physical Embodiment Neural State Sense Embeddings

April 26, 2024

Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces
Jiahong Wang, Yinwei Du, Stelian Coros, Bernhard Thomaszewski
Self Supervised Learning Latent Space Simulated Data Neural State Difference Subspace Mean Subspace

November 10, 2023

Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes
Elena Pastorelli, Alper Yegenoglu, Nicole Kolodziej, Willem Wybo, Francesco Simula, Sandra Diaz, Johan Frederik Storm, Pier Stanislao Paolucci
Full Model Neural Recording Neural State Social Isolation Pyramidal Neuron Distinct Functional Regime

October 23, 2023

One-hot Generalized Linear Model for Switching Brain State Discovery
Chengrui Li, Soon Ho Kim, Chris Rodgers, Hannah Choi, Anqi Wu
Generalized Linear Model Brain Dynamic Neural State Neural Interaction

October 11, 2023

A Convolutional Network Adaptation for Cortical Classification During Mobile Brain Imaging
Benjamin Cichy, Jamie Lukos, Mohammad Alam, J. Cortney Bradford, Nicholas Wymbs
Event Related Potential Cortical Surface Neural State

September 27, 2023

Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension
Yuhong Zhang, Qin Li, Sujal Nahata, Tasnia Jamal, Shih-kuen Cheng, Gert Cauwenberghs, Tzyy-Ping Jung
Eye Tracking Cognitive Science Reading Comprehension Semantic Understanding EEG Classification LLM Integrated Application Neural State

May 29, 2023

Taming AI Bots: Controllability of Neural States in Large Language Models
Stefano Soatto, Paulo Tabuada, Pratik Chaudhari, Tian Yu Liu
Controllability Condition Bot Generated Text Neural State AI Bot

March 26, 2023

Control of synaptic plasticity via the fusion of reinforcement learning and unsupervised learning in neural networks
Mohammad Modiri
Reinforcement Learning Neural Network Unsupervised Learning Hybrid Fusion Neural Activity Synaptic Plasticity Neural State

March 7, 2023

A topological classifier to characterize brain states: When shape matters more than variance
Aina Ferrà, Gloria Cecchini, Fritz-Pere Nobbe Fisas, Carles Casacuberta, Ignasi Cos
Object Shape Topological Data Analysis Topological Feature Variance Information Intrinsic Dimension Topological Metric Neural State

June 7, 2022

March 22, 2022

Constrained Parameter Inference as a Principle for Learning
Nasir Ahmad, Ellen Schrader, Marcel van Gerven
Stochastic Gradient Descent Back Propagation General Principle Parameter Inference Neural State

January 7, 2022

Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations
Dmitry Ivanov, Mikhail Kiselev, Denis Larionov
Reinforcement Learning Neural Network Reinforcement Learning Task Neural State Sparse Computation

December 21, 2021

fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits
Rotem Leibovitz, Jhonathan Osin, Lior Wolf, Guy Gurevitch, Talma Hendler
Functional Magnetic Resonance Imaging Neural State Life Event

November 3, 2021

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer
Neural Activity Neural Recording Generate Quick Representational Similarity Self Supervised Approach Swap Distance Minimization Neural Generation Neural State

Neural State

Papers

Do graph neural network states contain graph properties?

BrainCodec: Neural fMRI codec for the decoding of cognitive brain states

TAVRNN: Temporal Attention-enhanced Variational Graph RNN Captures Neural Dynamics and Behavior

How the (Tensor-) Brain uses Embeddings and Embodiment to Encode Senses and Decode Symbols

Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces

Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes

One-hot Generalized Linear Model for Switching Brain State Discovery

A Convolutional Network Adaptation for Cortical Classification During Mobile Brain Imaging

Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension

Taming AI Bots: Controllability of Neural States in Large Language Models

Control of synaptic plasticity via the fusion of reinforcement learning and unsupervised learning in neural networks

A topological classifier to characterize brain states: When shape matters more than variance

Transfer learning to decode brain states reflecting the relationship between cognitive tasks

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics

Constrained Parameter Inference as a Principle for Learning

Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations

fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity