University of Washington

Papers and research supported in part or in whole by The Swartz Foundation

Stefano Recanatesi, Ulises Pereira-Obilinovic, Masayoshi Murakami, Zachary Mainen, Luca Mazzucato
Metastable attractors explain the variable timing of stable behavioral action sequences. Neuron. 110(1): 139-53.39 (2022).

Fereshteh Lagzi, Adrienne Fairhall.
Tuned inhibitory firing rate and connection weights as emergent network properties, BioRXiv (2022).

Matthew Farrell, Stefano Recanatesi, Guillaume Lajoie, and Eric Shea-Brown
Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion. Nature Machine Intelligence 4: 564-573 (2022).

Leenoy Meshulam, Jeffrey L. Gauthier, Carlos D. Brody, David W. Tank, and William Bialek.
Successes and failures of simplified models for a network of real neurons, arXiv preprint arXiv:2112.14735 (2021).

Stefano Recanatesi, Matthew Farrell, Guillaume Lajoie, Sophie Deneve, Mattia Rigotti, and Eric Shea-Brown
Predictive learning as a network mechanism for extracting low-dimensional latent space representations, Nature Communications, 12, 1417 (2021).

Stefano Recanatesi, Serene Brade, Vijay Balasubramanian, Nicholas Steinmetz, and Eric Shea-Brown
A scale-dependent measure of system dimensionality. bioRXiv (2020).

Doris Voina, Stefano Recanatesi, Brian Hu, Eric Shea-Brown, and Stefan Mihalas
Single circuit in V1 capable of switching contexts during movement using VIP population as a switch, BioRXiv (2020).

Merav Stern, Eric Shea-Brown, and Daniela Witten
Inferring Neural Population Spiking Rate from Wide-Field Calcium Imaging, BioRXiv, (2020).

Matthew Farrell, Stefano Recanatesi, R. Clay Reid, Stefan Mihalas, Eric Shea-Brown
Autoencoder networks extract latent variables and encode these variables in their connectomes, ArXiv (2020).

Merav Stern and Eric Shea-Brown
Network Dynamics Governed by Lyapunov Functions: From Memory to Classification, Spotlight in Trends in Neurosciences (2020).

David Dahmen, Stefano Recanatesi, Gabriel Ocker, Xiaoxuan Jia, Moritz Helias, Eric Shea-Brown
Strong coupling and local control of dimensionality across brain areas, BioRXiv (2020).

Stefano Recanatesi, Gabe Ocker, Michael Buice, and Eric Shea-Brown
Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity, PLOS Computational Biology 5(7): e1006446, (2019).

Matthew Farrell, Stefano Recanatesi, Guillaume Lajoie, and Eric Shea-Brown
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion, BioRXiv (2019).

Delahunt, Charles B., and J. Nathan Kutz
Putting a Bug in ML: The Moth Olfactory Network Learns to Read MNIST, Neural Networks: The Official Journal of the International Neural Network Society 118 (October): 54�64 (2019).

Stefano Recanatesi, Matthew Farrell, Madhu Advani, Timothy Moore, Guillaume Lajoie, Eric Shea-Brown
Dimensionality compression and expansion in deep neural networks, ArXiv 1906:00443, (2019).

Delahunt, Charles B., Jeffrey A. Riffell, and J. Nathan Kutz Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca Sexta Moth, With Applications to Neural Nets. Frontiers in Computational Neuroscience 12 (December): 102 (2018).

Delahunt, Charles B., Pedro D. Maia, and J. Nathan Kutz
Built to Last: Functional and Structural Mechanisms in the Moth Olfactory Network Mitigate Effects of Neural Injury, arXiv [q-bio.NC](2018) .

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