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 | Conference PDF | Archive PDF | Plain Text | 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 {orrenkt,dzoltowski,mdiamanti,cbrody,dwtank,jpilllow}@princeton.edu lucas.pinto@northwestern.edu |
| 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. |