Comparing noisy neural population dynamics using optimal transport distances
Authors: Amin Nejatbakhsh, Victor Geadah, Alex Williams, David Lipshutz
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we use the metric to compare models of neural responses in different regions of the motor system and to compare the dynamics of latent diffusion models for text-to-image synthesis. We apply our method to compare simplified models of motor systems (Sec. 4.2) and the dynamics of conditional latent diffusion models (Sec. 4.3). |
| Researcher Affiliation | Academia | 1 Center for Computation Neuroscience, Flatiron Institute 2 Applied and Computational Mathematics, Princeton University 3 Center for Neural Science, New York University 4 Department of Neuroscience, Baylor College of Medicine |
| Pseudocode | Yes | We provide an alternating minimization algorithm for computing the distance between two processes using their firstand second-order statistics (Appx. B). Algorithm 1: Alternating minimization for computing causal OT distance |
| Open Source Code | Yes | Source code: https://github.com/amin-nejat/netrep. |
| Open Datasets | No | To test our framework, we consider two pretrained text-to-image latent diffusion models (v1-1 and v1-2)3 trained to generate text-conditional images from noise (Rombach et al., 2022). Models were taken from https://huggingface.co/Comp Vis. The paper describes using public models to generate data for experiments, but does not provide access information for the generated data itself as a dataset, nor for other datasets used in their experiments. |
| Dataset Splits | No | For each prompt and each model, we generated 60 latent trajectories and decoded those trajectories into the image space (examples of two decoded trajectories are shown in Fig. 5 and for several other trajectories in Fig. 7 of the supplement). We repeated this process for 3 random seeds to use the within-category distances as a baseline. This provided 60 datasets (2 diffusion models, 10 prompts, 3 seeds per prompt) each containing 60 latent trajectories. The paper describes how data was generated for the experiments, not how a pre-existing dataset was split into training/validation/test sets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory amounts, or cloud computing instance types) are mentioned in the paper for running the experiments. |
| Software Dependencies | No | We implemented DSA using the code provided in the Git Hub respository https://github.com/mitchellostrow/DSA. The paper mentions using code from a GitHub repository for DSA, but it does not specify software dependencies with version numbers for their own methodology or the overall experimental setup. |
| Experiment Setup | Yes | For DSA we chose the hyperparameters n delays = 9, rank = 10. We fixed all the other hyperparameters to the following for this and all other DSA experiments: delay interval = 1, lr = 0.01, iters = 1000. ... we first projected each set of latent trajectories onto its top 10 principal components (PCs) before computing the distances between the 10-dimensional stochastic trajectories. |