David Sussillo

According to our database, David Sussillo authored at least 24 papers between 2004 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

One dimensional approximations of neuronal dynamics reveal computational strategy.

Connor Brennan, Adeeti Aggarwal, Rui Pei, David Sussillo, Alex Proekt

PLoS Comput. Biol., January, 2023

Analyzing Populations of Neural Networks via Dynamical Model Embedding.

Jordan Cotler, Kai Sheng Tai, Felipe Hernández, Blake Elias, David Sussillo

CoRR, 2023

2022

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition.

Aran Nayebi, Javier Sagastuy-Breña, Daniel M. Bear, Kohitij Kar, Jonas Kubilius, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

Neural Comput., 2022

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution.

Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy P. Lillicrap, Adam H. Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Tsao

CoRR, 2022

2021

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems.

Jimmy T. H. Smith, Scott W. Linderman, David Sussillo

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Reverse engineering learned optimizers reveals known and novel mechanisms.

Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The geometry of integration in text classification RNNs.

Kyle Aitken, Vinay Venkatesh Ramasesh, Ankush Garg, Yuan Cao, David Sussillo, Niru Maheswaranathan

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Organizing recurrent network dynamics by task-computation to enable continual learning.

Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

How recurrent networks implement contextual processing in sentiment analysis.

Niru Maheswaranathan, David Sussillo

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics.

Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

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

Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

FixedPointFinder: A Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks.

Matthew D. Golub, David Sussillo

J. Open Source Softw., 2018

Task-Driven Convolutional Recurrent Models of the Visual System.

Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Dataset and Architecture for Visual Reasoning with a Working Memory.

Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, David Sussillo

Proceedings of the Computer Vision - ECCV 2018, 2018

Recurrent Segmentation for Variable Computational Budgets.

Lane McIntosh, Niru Maheswaranathan, David Sussillo, Jonathon Shlens

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

2017

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability.

Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo

Proceedings of the 34th International Conference on Machine Learning, 2017

Capacity and Trainability in Recurrent Neural Networks.

Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

LFADS - Latent Factor Analysis via Dynamical Systems.

David Sussillo, Rafal Józefowicz, L. F. Abbott, Chethan Pandarinath

CoRR, 2016

Intelligible Language Modeling with Input Switched Affine Networks.

Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo

CoRR, 2016

An Online Sequence-to-Sequence Model Using Partial Conditioning.

Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2014

Information Systems Opportunities in Brain-Machine Interface Decoders.

Jonathan C. Kao, Sergey D. Stavisky, David Sussillo, Paul Nuyujukian, Krishna V. Shenoy

Proc. IEEE, 2014

Random Walks: Training Very Deep Nonlinear Feed-Forward Networks with Smart Initialization.

David Sussillo

CoRR, 2014

2013

Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks.

David Sussillo, Omri Barak

Neural Comput., 2013

2004

Spectrogram Analysis of Genomes.

David Sussillo, Anshul Kundaje, Dimitris Anastassiou

EURASIP J. Adv. Signal Process., 2004