Learning Time-Invariant Representations for Individual Neurons from Population Dynamics
Authors: Lu Mi, Trung Le, Tianxing He, Eli Shlizerman, Uygar Sümbül
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our method on a public multimodal dataset of mouse cortical neuronal activity and transcriptomic labels. We report > 35% improvement in predicting the transcriptomic subclass identity and > 20% improvement in predicting class identity with respect to the state-of-the-art. |
| Researcher Affiliation | Collaboration | Lu Mi1,2, , Trung Le2, , Tianxing He2, Eli Shlizerman2, Uygar Sümbül1 1 Allen Institute for Brain Science 2 University of Washington {lu.mi,uygars}@alleninstitute.org {tle45, shlizee}@uw.edu goosehe@cs.washington.edu |
| Pseudocode | Yes | Appendix F Pseudo Code Our Neu PRINT framework includes three main components: an implicit dynamical system that uses the state-of-the-art transformer architecture to model neural dynamics; an optimization framework that fits the dynamical model and learns time-invariant representations for neurons; a supervised learning framework to train the downstream classifiers for subclass and class prediction, taking the learned time-invariant representations as inputs. The pseudo code for these three components is listed as follows: |
| Open Source Code | Yes | We released our software (https://github.com/lumimim/NeuPRINT/) for reproducibility. |
| Open Datasets | Yes | We use a recent, public multimodal dataset to train and demonstrate our model: Bugeon et al. [6] obtained population activity recordings from the mouse primary visual cortex (V1) via calcium imaging, followed by single-cell spatial transcriptomics of the tissue and registration of the two image sets to each other to identify the cells across the two experiments. |
| Dataset Splits | Yes | the neurons with subclass labels from all sessions are randomly split into train, validation and test neurons with a proportion of 80% : 10% : 10%. |
| Hardware Specification | Yes | All optimizations are performed on one NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions software like PyTorch, scikit-learn, Adam optimizer, and suite2p, but it does not provide specific version numbers for these software dependencies (e.g., 'PyTorch 1.9' or 'scikit-learn 0.24'). |
| Experiment Setup | Yes | Training details: For the objective function to predict the activity, we explore both mean squared error (MSE) and negative log likelihood (NLL) with a Gaussian distribution. To train the dynamical model and representation of neurons, we use a 64-dimensional embedding for the time-invariant representation. The temporal trial window size is 200 steps for the linear, nonlinear models, recurrent network and transformer. The batch size is 1024. We use the Adam optimizer [45] with a learning rate of 10-3. |