Flow-field inference from neural data using deep recurrent networks
Authors: Timothy Doyeon Kim, Thomas Zhihao Luo, Tankut Can, Kamesh Krishnamurthy, Jonathan W. Pillow, Carlos D Brody
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using spike train data from frontal brain regions of rats performing an auditory decision-making task, we demonstrate that FINDR performs competitively with existing methods in capturing the heterogeneous responses of individual neurons. When trained to disentangle task-relevant and irrelevant activity, FINDR uncovers interpretable low-dimensional dynamics. These dynamics can be visualized as flow fields and attractors, enabling direct tests of attractor-based theories of neural computation. We suggest FINDR as a powerful method for revealing the low-dimensional task-relevant dynamics of neural populations and their associated computations. |
| Researcher Affiliation | Academia | 1Princeton Neuroscience Institute, Princeton, NJ 2Present address: Allen Institute & University of Washington, Seattle, WA 3School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 4Present address: Department of Physics, Emory University, GA 5Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 6Howard Hughes Medical Institute, Princeton University, Princeton, NJ. Correspondence to: Timothy Doyeon Kim <EMAIL>, Carlos D. Brody <EMAIL>. |
| Pseudocode | No | The paper describes the model architecture and optimization process through textual descriptions and mathematical equations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available as a Git Hub repository: https://github.com/Brody-Lab/findr. |
| Open Datasets | Yes | We applied FINDR... to a dataset comprising 67 choice-selective neurons, selected from a larger population of 464 simultaneously recorded neurons from dorsomedial frontal cortex (dm FC) and medial prefrontal cortex (m PFC) of a rat engaged in a decision-making task across 448 trials (Luo et al., 2023). |
| Dataset Splits | Yes | We held out 13 neurons (about 20%) from this dataset, and partitioned the dataset into 5 different folds, each containing a subset of trials in random order. ... We used 3 of these folds for training, 1 fold for validation, and the remaining 1 fold for testing. We evaluated the 5-fold cross-validated log-likelihood of held-out neural activity to measure model performance. |
| Hardware Specification | No | We thank Jonathan Halverson for help with using the Princeton HPC clusters. While 'Princeton HPC clusters' indicates a computing resource, it lacks specific hardware details like GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | For SLDS and r SLDS, we used code from https://github.com/lindermanlab/ssm. For auto LFADS, we used code from https://github.com/arsedler9/lfads-torch, with hyperparameter search configurations in configs/pbt.yaml. For GPFA, we used Elephant: https://github.com/Neural Ensemble/ elephant. For CEBRA, we used code from https://github.com/Adaptive Motor Control Lab/cebra. We fit a Euclidean-distance CEBRA-Time model using hyperparameters from https://cebra.ai/docs/demo_ notebooks/CEBRA_best_practices.html#Items-to-consider, but with changes to three hyperparameters (model architecture="offset10-model-mse", max iterations=1000, output dimension=2). While specific software packages and their repositories are mentioned, no version numbers are provided for these tools or for any core programming languages or libraries (e.g., Python, PyTorch). |
| Experiment Setup | Yes | We train for a total of 3000 epochs and minimize loss using mini-batch gradient descent with warm restart (Loshchilov & Hutter, 2017). The learning rate increases from 0 to η linearly for 10 epochs every Dcyclei = 2i 1D epochs, where i goes from 1 to iend. After the 10 epochs, the learning rate decays in a cosine manner, where at Dcyclei, the learning rate becomes 0. iend is determined by the minimum Piend i=1 Dcyclei which is greater than or equal to 3000. D is set to be 200. ... Here, η {10 2.0, 10 1.625, 10 1.25, 10 0.875, 10 0.5}, HFNN {30, 50, 100}, and HRNN {50, 100, 200}. ... We set the coefficient of the ℓ2 regularization on the weights of all model parameters to be 10 7. ... We set the time constant τ = 0.1s. We set β = 2. We set the number of trials in a mini-batch to be 25. We set the momentum in mini-batch gradient descent to be 0.9. We perform annealing to the KL term in Equation (22). Specifically, the KL term is multiplied by 1 0.99iteration #. |