One-hot Generalized Linear Model for Switching Brain State Discovery
Authors: Chengrui Li, Soon Ho Kim, Chris Rodgers, Hannah Choi, Anqi Wu
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTAL EVALUATION Models for comparison. We will compare our methods and state-of-the-art baseline methods on one simulated data and two real neural datasets |
| Researcher Affiliation | Academia | 1 School of Computational Science & Engineering Georgia Institute of Technology, Atlanta, GA 30305, USA {cnlichengrui,anqiwu}@gatech.edu 2 School of Mathematics Georgia Institute of Technology, Atlanta, GA 30305, USA {soonhokim,hannahch}@gatech.edu 3 Department of Neurosurgery, School of Medicine Emory University, Atlanta, GA 30322, USA christopher.rodgers@emory.edu |
| Pseudocode | No | The paper describes inference algorithms mathematically but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/JerrySoybean/onehot-hmmglm. |
| Open Datasets | Yes | PFC-6 dataset (Peyrache et al., 2018; 2009)1. Neural spike trains were collected while a rat learned a behavioral contingency task. During recording, the animal performed a trial for about 4 secs and then took a short break for about 24 secs. The spike train data used for learning and testing is segmented from the long session. Each sequence starts from 5 seconds before a behavior start and lasts for 10 seconds after the start. Hence, each sequence corresponds to a behavioral trial. We use 2/3 of the neural sequences as the training set and the remaining 1/3 as the test set. The neural spikes are binned into 750 time bins with bin size = 20 ms. |
| Dataset Splits | No | For each trial, we train different models on the first 10 sequences and test on the remaining 10 sequences. (Simulated data) and We use 2/3 of the neural sequences as the training set and the remaining 1/3 as the test set. (PFC-6 dataset) and The first 30 trials are used in the analysis of each session, of which 10 randomly selected trials form the test set when evaluating the test log-likelihood, and the remaining 20 trials are used for training the model. (Barrel Cortex data). No explicit validation split information is provided. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory specifications, or cloud computing resources used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that were used for the experiments. |
| Experiment Setup | Yes | In our model, τ is used to force the soft one-hot close to one corner of the simplex, so we tried τ ∈ {0.1, 0.2, 0.5}, and found that the result of the one-hot HMM-GLM is not sensitive to τ in this range. Given that the selection of τ is insensitive to different datasets, we fix τ = 0.2, which is a common moderate choice. (2) Generative hyperparameters {µw, σ2w, µb, σ2b}: we chose µw = 5, σw = 2 and µb = 0, σb = 2 since this set provides noninformative priors for the weight strength and the background intensity in GLMs, and hence the inference is insensitive to different datasets. |