110 citations · 298 across the 13 of their papers we have counts for
4 papers · 1 filter
Persistent learning signals and working memory without continuous attractors
Il Memming Park, Ábel Ságodi, Piotr Aleksander Sokół
Neural dynamical systems with stable attractor structures, such as point attractors and continuous attractors, are hypothesized to underlie meaningful temporal behavior that requir…
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains
Matthew Dowling, Yuan Zhao, Il Memming Park
Latent Gaussian process (GP) models are widely used in neuroscience to uncover hidden state evolutions from sequential observations, mainly in neural activity recordings. While lat…
Real-Time Variational Method for Learning Neural Trajectory and its Dynamics
Matthew Dowling, Yuan Zhao, Il Memming Park
Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorith…
Spectral learning of Bernoulli linear dynamical systems models
Iris R. Stone, Yotam Sagiv, Il Memming Park +1
Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which ar…