38 citations · 110 across the 9 of their papers we have counts for
10 papers
Bits per Spike as a Betting Game: An Interpretable Unit for Held-Out Log-Likelihood in Neural Data Analysis
Alex H. Williams
Held-out log-likelihood is the standard currency for comparing statistical models of neural spike trains, and is often reported as bits per spike relative to a homogeneous Poisson…
Estimating Noise Correlations Across Continuous Conditions With Wishart Processes
Amin Nejatbakhsh, Isabel Garon, Alex H Williams
The signaling capacity of a neural population depends on the scale and orientation of its covariance across trials. Estimating this "noise" covariance is challenging and is thought…
Representational dissimilarity metric spaces for stochastic neural networks
Lyndon R. Duong, Jingyang Zhou, Josue Nassar +3
Quantifying similarity between neural representations -- e.g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research. Existing methods…
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models
Yixin Wang, Anthony Degleris, Alex H. Williams +1
Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural…
Generalized Shape Metrics on Neural Representations
Alex H. Williams, Erin Kunz, Simon Kornblith +1
Understanding the operation of biological and artificial networks remains a difficult and important challenge. To identify general principles, researchers are increasingly interest…
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…