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 [1].
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds
Authors: Anne Draelos, Pranjal Gupta, Na Young Jun, Chaichontat Sriworarat, John Pearson
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrated the performance of Bubblewrap on both simulated non-linear dynamical systems and experimental neural data. We compared these results to two existing online learning models for neural data, both of which are based on dynamical systems [30, 32]. For each data set, we gave each model the same data as reduced by random projections and pro SVD. For comparisons across models, we quantified overall model performance by taking the mean log predictive probability over the last half of each data set (Table 1). |
| Researcher Affiliation | Academia | Anne Draelos Biostatistics & Bioinformatics Duke University EMAIL Pranjal Gupta Psychology & Neuroscience Duke University EMAIL Na Young Jun Neurobiology Duke University EMAIL Chaichontat Sriworarat Biomedical Engineering Duke University EMAIL John Pearson Biostatistics & Bioinformatics Electrical & Computer Engineering Neurobiology Psychology & Neuroscience Duke University EMAIL |
| Pseudocode | Yes | Algorithm 1 Procrustean SVD (pro SVD) |
| Open Source Code | Yes | Our implementation of Bubblewrap, as well as code to reproduce our experiments, is open-source and available online at http://github.com/pearsonlab/Bubblewrap. |
| Open Datasets | Yes | For experimental data, we used four publicly available datasets from a range of applications: 1) trial-based spiking data recorded from primary motor cortex in monkeys performing a reach task [48, 49] preprocessed by performing online j PCA [49]; 2) continuous video data and 3) trial-based wide-field calcium imaging from a rodent decision-making task [50, 51]; 4) high-throughput Neuropixels data [52, 53]. |
| Dataset Splits | No | For comparisons across models, we quantified overall model performance by taking the mean log predictive probability over the last half of each data set (Table 1). The paper does not specify percentages or counts for training/validation/test splits, nor a detailed splitting methodology. |
| Hardware Specification | No | The paper mentions training on a GPU, but does not provide specific hardware details such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions 'JAX [54]' but does not provide specific version numbers for JAX or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | Given: Hyperparameters λj, j, βt, forgetting rate "t, step size δ, initial data buffer M 2: Initialize with {x1 . . . x M}: µj µ, j , aij 1 N . ...trained this model to maximize L(A, µ, ) using Adam [47], enforcing parameter constraints by replacing them with unconstrained variables aij and lower triangular Lj with positive diagonal. |