101 citations · 136 across the 9 of their papers we have counts for
9 papers
Natural continual learning: success is a journey, not (just) a destination
Ta-Chu Kao, Kristopher T. Jensen, Gido M. van de Ven +2
Biological agents are known to learn many different tasks over the course of their lives, and to be able to revisit previous tasks and behaviors with little to no loss in performan…
Automatic differentiation of Sylvester, Lyapunov, and algebraic Riccati equations
Ta-Chu Kao, Guillaume Hennequin
Sylvester, Lyapunov, and algebraic Riccati equations are the bread and butter of control theorists. They are used to compute infinite-horizon Gramians, solve optimal control proble…
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi +1
A common problem in neuroscience is to elucidate the collective neural representations of behaviorally important variables such as head direction, spatial location, upcoming moveme…
Efficient Communication over Complex Dynamical Networks: The Role of Matrix Non-Normality
Giacomo Baggio, Virginia Rutten, Guillaume Hennequin +1
In both natural and engineered systems, communication often occurs dynamically over networks ranging from highly structured grids to largely disordered graphs. To use, or comprehen…
Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability
Laurence Aitchison, Guillaume Hennequin, Mate Lengyel
Neural responses in the cortex change over time both systematically, due to ongoing plasticity and learning, and seemingly randomly, due to various sources of noise and variability…
Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model
Rodrigo Echeveste, Guillaume Hennequin, Máté Lengyel
The Gaussian scale mixture model (GSM) is a simple yet powerful probabilistic generative model of natural image patches. In line with the well-established idea that sensory process…
Characterizing variability in nonlinear recurrent neuronal networks
Guillaume Hennequin, Máté Lengyel
In this note, we develop semi-analytical techniques to obtain the full correlational structure of a stochastic network of nonlinear neurons described by rate variables. Under the a…
Fast sampling for Bayesian inference in neural circuits
Guillaume Hennequin, Laurence Aitchison, Máté Lengyel
Time is at a premium for recurrent network dynamics, and particularly so when they are stochastic and correlated: the quality of inference from such dynamics fundamentally depends…
Nonnormal amplification in random balanced neuronal networks
Guillaume Hennequin, Tim P. Vogels, Wulfram Gerstner
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to the network in two distinct ways. One is induced by the presence of near-critica…