110 citations · 298 across the 13 of their papers we have counts for
13 papers
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…
Metastable dynamics of neural circuits and networks
Braden A. W. Brinkman, Han Yan, Arianna Maffei +4
Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. Th…
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Felix Pei, Joel Ye, David Zoltowski +13
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this…
On 1/n neural representation and robustness
Josue Nassar, Piotr Aleksander Sokol, SueYeon Chung +2
Understanding the nature of representation in neural networks is a goal shared by neuroscience and machine learning. It is therefore exciting that both fields converge not only on…
Rescuing neural spike train models from bad MLE
Diego M. Arribas, Yuan Zhao, Il Memming Park
The standard approach to fitting an autoregressive spike train model is to maximize the likelihood for one-step prediction. This maximum likelihood estimation (MLE) often leads to…
Birhythmic Analog Circuit Maze: A Nonlinear Neurostimulation Testbed
Ian D. Jordan, Il Memming Park
Brain dynamics can exhibit narrow-band nonlinear oscillations and multistability. For a subset of disorders of consciousness and motor control, we hypothesize that some symptoms or…
Streaming Variational Monte Carlo
Yuan Zhao, Josue Nassar, Ian Jordan +2
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simul…
Gated recurrent units viewed through the lens of continuous time dynamical systems
Ian D. Jordan, Piotr Aleksander Sokol, Il Memming Park
Gated recurrent units (GRUs) are specialized memory elements for building recurrent neural networks. Despite their incredible success on various tasks, including extracting dynamic…
Variational online learning of neural dynamics
Yuan Zhao, Il Memming Park
New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underl…
Black box variational inference for state space models
Evan Archer, Il Memming Park, Lars Buesing +2
Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure…