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Found 8 papers, 4 papers with code
Meta-Dynamical State Space Models for Integrative Neural Data Analysis
However, there has been limited work exploiting the shared structure in neural activity during similar tasks for learning latent dynamics from neural recordings.
Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments
We emphasize the importance of large-scale integrative neuroscience initiatives and the role of meta-learning in overcoming these challenges.
Representational dissimilarity metric spaces for stochastic neural networks
Quantifying similarity between neural representations -- e. g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research.
BAM: Bayes with Adaptive Memory
Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs.
BAM: Bayes Augmented with Memory
Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs.
On 1/n neural representation and robustness
In this work, we investigate the latter by juxtaposing experimental results regarding the covariance spectrum of neural representations in the mouse V1 (Stringer et al) with artificial neural networks.
Streaming Variational Monte Carlo
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series.
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
Many real-world systems studied are governed by complex, nonlinear dynamics.
