Extracting low-dimensional dynamics from multiple large-scale neural
population recordings by learning to predict correlations

Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations

6 November 2017

M. Nonnenmacher

Srinivas C. Turaga

Jakob H. Macke

Papers citing "Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations"

5 / 5 papers shown

Title
Spectral learning of Bernoulli linear dynamical systems models Iris R. Stone Yotam Sagiv Il Memming Park Jonathan W. Pillow 40 1 0 03 Mar 2023
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci C. Pandarinath 30 10 0 29 Oct 2021
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity Felix Pei Joel Ye D. Zoltowski Anqi Wu Raeed H. Chowdhury ... L. Miller Jonathan W. Pillow Il Memming Park Eva L. Dyer C. Pandarinath 55 87 0 09 Sep 2021
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex Benjamin R. Cowley Jonathan W. Pillow 29 10 0 13 Jun 2020
Analyzing biological and artificial neural networks: challenges with opportunities for synergy? David Barrett Ari S. Morcos Jakob H. Macke AI4CE 27 110 0 31 Oct 2018