475 citations · 517 across the 8 of their papers we have counts for
9 papers
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…
On the role of time in perceptual decision making
Máté Lengyel, Ádám Koblinger, Marjena Popović +1
According to the dominant view, time in perceptual decision making is used for integrating new sensory evidence. Based on a probabilistic framework, we investigated the alternative…
The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics
Laurence Aitchison, Máté Lengyel
Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses…
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…