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Found 9 papers, 3 papers with code
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization
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 observations and the unknown parameters.
Denoising Normalizing Flow
Here we propose a novel method - called Denoising Normalizing Flow (DNF) - that estimates the density on the low-dimensional manifold while learning the manifold as well.
Density estimation on low-dimensional manifolds: an inflation-deflation approach
We also show that, if the embedding dimension is much larger than the manifold dimension, noise in the normal space can be well approximated by Gaussian noise.
The Hitchhiker's Guide to Nonlinear Filtering
From there we continue our journey through discrete-time models, which is usually encountered in machine learning, and generalize to and further emphasize continuous-time filtering theory.
Methodology
Propagation of spiking moments in linear Hawkes networks
The present paper provides exact mathematical expressions for the high-order moments of spiking activity in a recurrently-connected network of linear Hawkes processes.
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes
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 parameter.
Synaptic plasticity as Bayesian inference
They then use that uncertainty to adjust their learning rates, with more uncertain weights having higher learning rates.
Sequence learning with hidden units in spiking neural networks
We consider a statistical framework in which recurrent networks of spiking neurons learn to generate spatio-temporal spike patterns.
Know Thy Neighbour: A Normative Theory of Synaptic Depression
Synapses exhibit an extraordinary degree of short-term malleability, with release probabilities and effective synaptic strengths changing markedly over multiple timescales.
