M. Farrell

发表

Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion

Eric T. Shea-Brown, Guillaume Lajoie, Stefano Recanatesi, 2022, Nature Machine Intelligence.

Signatures and mechanisms of 1 low-dimensional neural predictive 2 manifolds 3

S. Denéve, Mattia Rigotti, Guillaume Lajoie, 2018 .

Dimensionality compression and expansion in Deep Neural Networks

Madhu S. Advani, Guillaume Lajoie, Stefano Recanatesi, 2019, ArXiv.

Autoencoder networks extract latent variables and encode these variables in their connectomes

Eric Shea-Brown, Stefan Mihalas, Matthew Farrell, 2020, bioRxiv.

Gaussian Process Regression Models for Predicting Stock Trends

M. Todd Farrell, Andrew Correa, M. Farrell, 2007 .

Estimation of Ground Reaction Force and Zero Moment Point on a Powered Ankle-Foot Prosthesis

H. Herr, E.C. Martinez-Villalpando, M. Farrell, 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Predictive learning as a network mechanism for extracting low-dimensional latent space representations

Eric T. Shea-Brown, S. Denéve, Mattia Rigotti, 2021, Nature Communications.

A method to determine the optimal features for control of a powered lower-limb prostheses

Hugh M. Herr, Matthew Todd Farrell, H. Herr, 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?

Shubhendu Trivedi, C. Pehlevan, Blake Bordelon, 2021, ICLR.

Dynamic compression and expansion in a classifying recurrent network

Eric Shea-Brown, Matthew Farrell, Stefano Recanatesi, 2019, bioRxiv.

Angular momentum primitives for human turning: Control implications for biped robots

Hugh M. Herr, Matthew Todd Farrell, H. Herr, 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.

Signatures and mechanisms of low-dimensional neural predictive manifolds

Eric Shea-Brown, Matthew Farrell, Mattia Rigotti, 2018, bioRxiv.

Angular momentum in turns and abrupt starts : strategies for bipedal balance control

Matthew Todd Farrell, M. Farrell, 2009 .

Predictive learning as a network mechanism for extracting low-dimensional latent space representations

Eric Shea-Brown, Matthew Farrell, Mattia Rigotti, 2021, Nature Communications.

Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing

C. Pehlevan, M. Farrell, 2023, bioRxiv.

From lazy to rich to exclusive task representations in neural networks and neural codes

Stefano Recanatesi, M. Farrell, E. Shea-Brown, 2023, Current Opinion in Neurobiology.

Predictive learning extracts latent space representations from sensory observations

S. Denéve, Mattia Rigotti, Guillaume Lajoie, 2019 .

Signatures and mechanisms of 1 low-dimensional neural predictive 2 manifolds 3

S. Denéve, Mattia Rigotti, Guillaume Lajoie, 2018 .