102 citations · 179 across the 11 of their papers we have counts for
11 papers
Spectral theory for population density dynamics of spiking neurons with refractoriness
Luca Falorsi, Gianni Valerio Vinci., Maurizio Mattia
Incorporating an absolute refractory period into the population density approach for spiking neurons remains an open problem, despite evidence that refractoriness can strongly affe…
Bilinear gating of motor primitives: a principle linking dendritic computation to rapid goal-directed adaptation
Cristiano Capone, Luca Falorsi, Andrea Ciardiello +1
Movement requires the motor cortex to specify both \emph{what} action to produce and \emph{which goal} it serves, yet how individual neurons separate these factors is not understoo…
Unified Policy Value Decomposition for Rapid Adaptation
Cristiano Capone, Luca Falorsi, Andrea Ciardiello +1
Rapid adaptation in complex control systems remains a central challenge in reinforcement learning. We introduce a framework in which policy and value functions share a low-dimensio…
Non-stationary dynamics of interspike intervals in neuronal populations
Luca Falorsi, Gianni V. Vinci, Maurizio Mattia
We study the joint dynamics of membrane potential and time since the last spike in a population of integrate-and-fire neurons using a population density framework. This leads to a…
Adaptive behavior with stable synapses
Cristiano Capone, Luca Falorsi
Behavioral changes in animals and humans, as a consequence of an error or a verbal instruction, can be extremely rapid. Improvement in behavioral performances are usually associate…
Continuous normalizing flows on manifolds
Luca Falorsi
Normalizing flows are a powerful technique for obtaining reparameterizable samples from complex multimodal distributions. Unfortunately, current approaches are only available for t…
Neural Ordinary Differential Equations on Manifolds
Luca Falorsi, Patrick Forré
Normalizing flows are a powerful technique for obtaining reparameterizable samples from complex multimodal distributions. Unfortunately current approaches fall short when the under…
Reparameterizing Distributions on Lie Groups
Luca Falorsi, Pim de Haan, Tim R. Davidson +1
Reparameterizable densities are an important way to learn probability distributions in a deep learning setting. For many distributions it is possible to create low-variance gradien…
Topological Constraints on Homeomorphic Auto-Encoding
Pim de Haan, Luca Falorsi
When doing representation learning on data that lives on a known non-trivial manifold embedded in high dimensional space, it is natural to desire the encoder to be homeomorphic whe…
Explorations in Homeomorphic Variational Auto-Encoding
Luca Falorsi, Pim de Haan, Tim R. Davidson +4
The manifold hypothesis states that many kinds of high-dimensional data are concentrated near a low-dimensional manifold. If the topology of this data manifold is non-trivial, a co…
Hyperspherical Variational Auto-Encoders
Tim R. Davidson, Luca Falorsi, Nicola De Cao +2
The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and p…