22 citations · 62 across the 8 of their papers we have counts for
10 papers
Leveraging unlabelled data for generalizable neural population decoding
Ximeng Mao, Nanda H. Krishna, Avery Hee-Woon Ryoo +2
Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent work has shown that tokenizing neural da…
JEDI: Jointly Embedded Inference of Neural Dynamics
Anirudh Jamkhandi, Ali Korojy, Olivier Codol +2
Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to map the mechanisms of the brain's flexib…
POCO: Scalable Neural Forecasting through Population Conditioning
Yu Duan, Hamza Tahir Chaudhry, Misha B. Ahrens +4
Predicting future neural activity is a core challenge in modeling brain dynamics, with applications ranging from scientific investigation to closed-loop neurotechnology. While rece…
Generalizable, real-time neural decoding with hybrid state-space models
Avery Hee-Woon Ryoo, Nanda H. Krishna, Ximeng Mao +4
Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-computer interfaces, where models are subje…
Expressivity of Neural Networks with Random Weights and Learned Biases
Ezekiel Williams, Alexandre Payeur, Avery Hee-Woon Ryoo +4
Landmark universal function approximation results for neural networks with trained weights and biases provided the impetus for the ubiquitous use of neural networks as learning mod…
A Unified, Scalable Framework for Neural Population Decoding
Mehdi Azabou, Vinam Arora, Venkataramana Ganesh +7
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration…
Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles
Justin Jude, Matthew G. Perich, Lee E. Miller +1
Decoding stimuli or behaviour from recorded neural activity is a common approach to interrogate brain function in research, and an essential part of brain-computer and brain-machin…
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation
Justin Jude, Matthew G Perich, Lee E Miller +1
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.…
Targeted Neural Dynamical Modeling
Cole Hurwitz, Akash Srivastava, Kai Xu +4
Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously mea…
Machine learning for neural decoding
Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury +3
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and e…