Research map / Probabilistic and scientific modeling / Dynamical systems
Neural Latents Benchmark ‘21: Evaluating latent variable models of neural population activity
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a deep neural network (AutoLFADS) rather than the strongest non-deep baseline, SLDS | latent variable modeling · MC_Maze | co-smoothing (bits/spike): 0.219 -> 0.346 | Helped |
| Use a deep neural network (AutoLFADS) rather than the strongest non-deep baseline, SLDS | latent variable modeling · MC_Maze-L | co-smoothing (bits/spike): 0.290 -> 0.374 | Helped |
| Use a deep neural network (AutoLFADS) rather than the strongest non-deep baseline, SLDS | latent variable modeling · MC_Maze-M | co-smoothing (bits/spike): 0.210 -> 0.304 | Helped |
| Use AutoLFADS rather than NDT | latent variable modeling · MC_Maze-S | co-smoothing (bits/spike): 0.251 -> 0.301 | Helped |
| Use a deep neural network (AutoLFADS) rather than the strongest non-deep baseline, SLDS | latent variable modeling · MC_RTT | co-smoothing (bits/spike): 0.165 -> 0.192 | Helped |
| Use NDT rather than AutoLFADS | latent variable modeling · Area2_Bump | co-smoothing (bits/spike): 0.259 -> 0.267 | Helped |
| Use a deep neural network (AutoLFADS) rather than the strongest non-deep baseline, Smoothing | latent variable modeling · DMFC_RSG | co-smoothing (bits/spike): 0.120 -> 0.181 | Helped |
Closest papers by meaning
- Latent Diffusion for Neural Spiking Data (NeurIPS 2024 · similarity 0.90)
- BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation (ICLR 2025 · similarity 0.88)
- A universal probabilistic spike count model reveals ongoing modulation of neural variability (NeurIPS 2021 · similarity 0.88)
- Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors (NeurIPS 2025 · similarity 0.88)
- Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data (ICLR 2024 · similarity 0.88)
- Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics (NeurIPS 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.