Neural Dynamic

Neural dynamics research focuses on understanding how the activity of interconnected neurons generates behavior and cognition. Current efforts concentrate on developing and applying computational models, including recurrent neural networks, spiking neural networks, and variational autoencoders, to analyze neural data and infer underlying dynamical systems from diverse experimental modalities. This work aims to bridge the gap between observed neural activity and behavior, leading to improved understanding of brain function and informing the design of more biologically plausible and efficient artificial intelligence systems. Furthermore, these advancements have implications for applications such as brain-computer interfaces and the development of more robust and interpretable machine learning models.

Papers

April 1, 2022

Learnable latent embeddings for joint behavioral and neural analysis
Steffen Schneider, Jin Hwa Lee, Mackenzie Weygandt Mathis
Neural Representation Neural Activity Neural Dynamic Latent Embeddings Complex Behavior Neural Metric

March 29, 2022

Design strategies for controlling neuron-connected robots using reinforcement learning
Haruto Sawada, Naoki Wake, Kazuhiro Sasabuchi, Jun Takamatsu, Hirokazu Takahashi, Katsushi Ikeuchi
Reinforcement Learning Deep Reinforcement Learning Design Principle Context Dependent Neural Dynamic Robot Deployment

November 29, 2021

Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations
Semih Günel, Florian Aymanns, Sina Honari, Pavan Ramdya, Pascal Fua
Contrastive Learning Neural Representation Domain Gap Neural Activity Action Representation Neural Dynamic

Neural Dynamic

Papers

Learnable latent embeddings for joint behavioral and neural analysis

Design strategies for controlling neuron-connected robots using reinforcement learning

Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations