2 citations · 6 across the 8 of their papers we have counts for
10 papers
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization
Camille Gontier, Simone Carlo Surace, Igor Delvendahl +2
Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the…
A Unification of Weighted and Unweighted Particle Filters
Ehsan Abedi, Simone Carlo Surace, Jean-Pascal Pfister
Particle filters (PFs), which are successful methods for approximating the solution of the filtering problem, can be divided into two types: weighted and unweighted PFs. It is well…
Learning as filtering: implications for spike-based plasticity
Jannes Jegminat, Jean-Pascal Pfister
Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as o…
Asymptotically exact unweighted particle filter for manifold-valued hidden states and point process observations
Simone Carlo Surace, Anna Kutschireiter, Jean-Pascal Pfister
The filtering of a Markov diffusion process on a manifold from counting process observations leads to `large' changes in the conditional distribution upon an observed event, corres…
The Hitchhiker's Guide to Nonlinear Filtering
Anna Kutschireiter, Simone Carlo Surace, Jean-Pascal Pfister
Nonlinear filtering is the problem of online estimation of a dynamic hidden variable from incoming data and has vast applications in different fields, ranging from engineering, mac…
Gauge Freedom within the Class of Linear Feedback Particle Filters
Ehsan Abedi, Simone Carlo Surace
Feedback particle filters (FPFs) are Monte-Carlo approximations of the solution of the filtering problem in continuous time. The samples or particles evolve according to a feedback…
On the choice of metric in gradient-based theories of brain function
Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner +1
The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Since a cost function by itself does not predict how the brain finds its minima…
How to avoid the curse of dimensionality: scalability of particle filters with and without importance weights
Simone Carlo Surace, Anna Kutschireiter, Jean-Pascal Pfister
Particle filters are a popular and flexible class of numerical algorithms to solve a large class of nonlinear filtering problems. However, standard particle filters with importance…
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes
Simone Carlo Surace, Jean-Pascal Pfister
We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time…
A statistical model for in vivo neuronal dynamics
Simone Carlo Surace, Jean-Pascal Pfister
Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as t…