no code implementations • NeurIPS 2021 • David Liu, Mate Lengyel
We find that variability in these cells defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations.
no code implementations • NeurIPS 2018 • Alberto Bernacchia, Mate Lengyel, Guillaume Hennequin
Stochastic gradient descent (SGD) remains the method of choice for deep learning, despite the limitations arising for ill-behaved objective functions.
no code implementations • NeurIPS 2016 • Daniel McNamee, Daniel M. Wolpert, Mate Lengyel
Even in state-spaces of modest size, planning is plagued by the “curse of dimensionality”.
no code implementations • NeurIPS 2014 • Dylan Festa, Guillaume Hennequin, Mate Lengyel
The persistent and graded activity often observed in cortical circuits is sometimes seen as a signature of autoassociative retrieval of memories stored earlier in synaptic efficacies.
no code implementations • NeurIPS 2014 • Guillaume Hennequin, Laurence Aitchison, Mate Lengyel
Multiple lines of evidence support the notion that the brain performs probabilistic inference in multiple cognitive domains, including perception and decision making.
no code implementations • NeurIPS 2014 • Sina Tootoonian, Mate Lengyel
We study the early locust olfactory system in an attempt to explain its well-characterized structure and dynamics.
no code implementations • NeurIPS 2013 • Cristina Savin, Peter Dayan, Mate Lengyel
It has long been recognised that statistical dependencies in neuronal activity need to be taken into account when decoding stimuli encoded in a neural population.