4 citations · 4 across the 1 of their papers we have counts for
5 papers
Statistical Neuroscience in the Single Trial Limit
Alex H. Williams, Scott W. Linderman
Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cogniti…
Point process models for sequence detection in high-dimensional neural spike trains
Alex H. Williams, Anthony Degleris, Yixin Wang +1
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a long…
Universality and individuality in neural dynamics across large populations of recurrent networks
Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub +2
Task-based modeling with recurrent neural networks (RNNs) has emerged as a popular way to infer the computational function of different brain regions. These models are quantitative…
Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates
Anthony Degleris, Ben Antin, Surya Ganguli +1
Identifying recurring patterns in high-dimensional time series data is an important problem in many scientific domains. A popular model to achieve this is convolutive nonnegative m…
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
Niru Maheswaranathan, Alex Williams, Matthew D. Golub +2
Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes. Given a trained recurrent network, we w…