Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays. Therefore, predicting behaviour from neural population recordings has been shown to be most effective when using latent variable models. Over time however, the activity of single neurons can drift, and different neurons will be recorded due to movement of implanted neural probes. This means that a decoder trained to predict behaviour on one day performs worse when tested on a different day. On the other hand, evidence suggests that the latent dynamics underlying behaviour may be stable even over months and years. Based on this idea, we introduce a model capable of inferring behaviourally relevant latent dynamics from previously unseen data recorded from the same animal, without any need for decoder recalibration. We show that unsupervised domain adaptation combined with a sequential variational autoencoder, trained on several sessions, can achieve good generalisation to unseen data and correctly predict behaviour where conventional methods fail. Our results further support the hypothesis that behaviour-related neural dynamics are low-dimensional and stable over time, and will enable more effective and flexible use of brain computer interface technologies.
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
RIKEN Center for Advanced Intelligence Project (RIKEN AIP)（理化学研究所先进智能项目中心）；Advanced Telecommunications Research Institute International (ATR)（国际电气通信基础技术研究所）
Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching
Politecnico di Milano（米兰理工大学）；International Audio Laboratories Erlangen（埃尔朗根国际音频实验室）；Friedrich-Alexander-University Erlangen-Nurnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；Fraunhofer Institute for Integrated Circuits（弗劳恩霍夫集成电路研究所）
NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings
Northwestern University（西北大学）；University of Cambridge（剑桥大学）；Johns Hopkins University（约翰霍普金斯大学）；Columbia University（哥伦比亚大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Stanford University（斯坦福大学）
Unsupervised learning of MRI tissue properties using MRI physics models
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；California Institute of Technology（加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）
POCO: Scalable Neural Forecasting through Population Conditioning
EECS, MIT（MIT电子工程与计算机科学系）；SEAS, Harvard University（哈佛大学科学与工程系）；Janelia Research Campus（贾尔尼亚研究校园）；Harvard Medical School（哈佛医学院）；Kempner Institute（凯普纳研究所）；Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Stanford University（斯坦福大学）
Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes
Center for Machine Learning, Georgia Tech（佐治亚理工学院机器学习中心）；Department of Biomedical Engineering, Georgia Tech and Emory University（佐治亚理工学院与埃默里大学生物医学工程系）；AE Studio（AE工作室）；Department of Neurosurgery, Emory University School of Medicine（埃默里大学医学院神经外科）
Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling
Ming Hsieh Department of Electrical and Computer Engineering（明希斯电气与计算机工程系）；Thomas Lord Department of Computer Science（托马斯·劳德计算机科学系）；Alfred E. Mann Department of Biomedical Engineering（阿尔弗雷德·E·曼生物医学工程系）；Neuroscience Graduate Program University of Southern California（神经科学研究生项目美国南加州大学）
