Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems
Authors: Orren Karniol-Tambour, David M. Zoltowski, E. Mika Diamanti, Lucas Pinto, Carlos D Brody, David W. Tank, Jonathan W. Pillow
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our model accurately recovers latent trajectories, vector fields underlying switching nonlinear dynamics, and cross-region communication profiles in three simulations. We then apply our method to two large-scale, multi-region neural datasets involving mouse decision-making. |
| Researcher Affiliation | Academia | Orren Karniol-Tambour, David M. Zoltowski, E. Mika Diamanti, Lucas Pinto , Carlos D. Brody, David W. Tank, Jonathan W. Pillow Princeton Neuroscience Institute, Northwestern University EMAIL EMAIL |
| Pseudocode | Yes | Algorithm Cosmoothing multiregion drop out training |
| Open Source Code | No | The paper mentions using a modified version of the SSM package from Linderman et al. (https://github.com/slinderman/ssm), but it does not provide a link or statement about open-sourcing the code for MR-SDS, the methodology described in this paper. |
| Open Datasets | Yes | We applied our method to calcium imaging data recorded in mice performing a sensory evidence accumulation task (Figure 4). In the task, a headfixed mouse runs on a linear track in virtual reality while columns ( towers ) are presented on both sides of the track (Pinto et al., 2019). |
| Dataset Splits | Yes | The Neural Latents Benchmark uses a 25% neuron drop out rate, and we apply this rate, with one modification we evaluate all models against a 25% drop out rate per region. |
| Hardware Specification | Yes | All experiments and analysis were run on a 28 CPU, 8 GPU (Ge Force RTX 2080 Ti) server. |
| Software Dependencies | No | The paper mentions using 'a modified version of the SSM package' but does not specify version numbers for this or any other software components used for the experiments. |
| Experiment Setup | Yes | Table 4: Parameters used for each of the experiments presented in the paper. LV: Switching Lotka-Voltera. DW: Double well. Meso: mesoscope. Wide: widefield. lr: learning rate. p2: trial dropout rate used for cosmoothing training. I: Identity mapping. L: linear layer. d: dimensionality of latents for each region. |