1 citations · 1 across the 3 of their papers we have counts for
5 papers
Memory by Design: Bayesian Layers for Sequence Modeling
Matthew Dowling, Hyungju Jeon, Cristina Savin +1
We introduce the \emph{design-model framework}: a way to derive efficient recurrent sequence maps from explicit assumptions about memory. A design model writes evidence into memory…
Integrative neurocybernetic modeling in the era of large-scale neuroscience
Il Memming Park, Ayesha Vermani, Gonzalo G. de Polavieja +12
Large-scale neuroscience is generating rich datasets across animals, brain areas, and behavioral contexts, yet our modeling efforts remain fragmented across isolated experiments. W…
Meta-Dynamical State Space Models for Integrative Neural Data Analysis
Ayesha Vermani, Josue Nassar, Hyungju Jeon +2
Learning shared structure across environments facilitates rapid learning and adaptive behavior in neural systems. This has been widely demonstrated and applied in machine learning…
Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments
Ayesha Vermani, Matthew Dowling, Hyungju Jeon +6
Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence…
Quantifying Signal-to-Noise Ratio in Neural Latent Trajectories via Fisher Information
Hyungju Jeon, Il Memming Park
Spike train signals recorded from a large population of neurons often exhibit low-dimensional spatio-temporal structure and modeled as conditional Poisson observations. The low-dim…