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BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS
TLDR
A structured Gaussian variational approximate posterior is proposed that carries the same intuition as the standard Kalman filter-smoother but permits us to use the same inference approach to approximate the posterior of much more general, nonlinear latent variable generative models.
Encoding and decoding in parietal cortex during sensorimotor decision-making
TLDR
This work examined the neural code in LIP at the level of individual spike trains using a statistical approach based on generalized linear models and derived an optimal decoder for heterogeneous, multiplexed LIP responses that could be implemented in biologically plausible circuits.
A comparison of binless spike train measures
TLDR
This paper presents a systematic comparison of several binless spike train measures in three simulated paradigms designed to address specific situations of interest in spike train analysis where the relevant feature may be in the form of firing rate, firing rate modulations, and/or synchrony.
An Information Theoretic Approach of Designing Sparse Kernel Adaptive Filters
TLDR
A systematic sparsification scheme is proposed, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters.
Bayesian Spike-Triggered Covariance Analysis
TLDR
An empirical Bayes method for selecting the number of features is described, and the model is extended to accommodate an arbitrary elliptical nonlinear response function, which results in a more powerful and more flexible model for feature space inference.
Functional dissection of signal and noise in MT and LIP during decision-making
TLDR
It is found that monkeys based their choices on evidence presented in early epochs of the motion stimulus and that substantial early weighting of motion was present in MT responses and that trial-by-trial variability in LIP did not depend on MT activity.
A Unified Framework for Quadratic Measures of Independence
TLDR
It is shown that by generalizing the inner product using a symmetric strictly positive-definite kernel and by choosing appropriate kernels, it is possible to reproduce a number of popular measures of independence.
Strictly Positive-Definite Spike Train Kernels for Point-Process Divergences
TLDR
This work explores strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate.
Variational Online Learning of Neural Dynamics
TLDR
A flexible online learning framework for latent non-linear state dynamics and filtered latent states is developed using the stochastic gradient variational Bayes approach and can incorporate non-trivial distributions of observation noise and has constant time and space complexity.
Gated Recurrent Units Viewed Through the Lens of Continuous Time Dynamical Systems
TLDR
A surprisingly rich repertoire of dynamical features that includes stable limit cycles (nonlinear oscillations), multi-stable dynamics with various topologies, and homoclinic bifurcations is found.
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