Alex H Williams | DeepAI

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∙ 08/22/2023

Estimating Noise Correlations in Neural Populations with Wishart Processes

The signaling capacity of a neural population depends on the scale and o...

0 Amin Nejatbakhsh, et al. ∙

∙ 11/21/2022

Representational dissimilarity metric spaces for stochastic neural networks

Quantifying similarity between neural representations – e.g. hidden laye...

0 Lyndon R. Duong, et al. ∙

∙ 10/27/2021

Generalized Shape Metrics on Neural Representations

Understanding the operation of biological and artificial networks remain...

0 Alex H Williams, et al. ∙

∙ 03/08/2021

Statistical Neuroscience in the Single Trial Limit

Individual neurons often produce highly variable responses over nominall...

0 Alex H Williams, et al. ∙

∙ 10/10/2020

Point process models for sequence detection in high-dimensional neural spike trains

Sparse sequences of neural spikes are posited to underlie aspects of wor...

2 Alex H Williams, et al. ∙

∙ 07/19/2019

Universality and individuality in neural dynamics across large populations of recurrent networks

Task-based modeling with recurrent neural networks (RNNs) has emerged as...

1 Niru Maheswaranathan, et al. ∙

∙ 06/29/2019

Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates

Identifying recurring patterns in high-dimensional time series data is a...

5 Anthony Degleris, et al. ∙

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