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].
Mutually Regressive Point Processes
Authors: Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed model on single and multi-neuronal spike train recordings. Results demonstrate that the proposed model, unlike existing point process models, can generate biologically-plausible spike trains, while still achieving competitive predictive likelihoods. |
| Researcher Affiliation | Academia | Ifigeneia Apostolopoulou Machine Learning Department Carnegie Mellon University EMAIL Scott Linderman Department of Statistics Stanford University EMAIL Kyle Miller Auton Lab Carnegie Mellon University EMAIL Artur Dubrawski Auton Lab Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Bayesian Inference for Mutually Regressive Point Processes |
| Open Source Code | Yes | The library is written in C++. Our code is available at https://github.com/ifiaposto/ Mutually-Regressive-Point-Processes |
| Open Datasets | Yes | We repeat the analysis on two datasets (Figure 2.b and Figure 2.c in [35]) for which PP-GLMs have failed in generating stable spiking dynamics. The data is publicly available and can be downloaded from the NSF-funded CRCNS data repository [51]. |
| Dataset Splits | No | The paper describes a temporal split for training and testing data ('[0, 13000] msec for learning' and '[13000, 26000] msec for testing') but does not explicitly mention or specify a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'C++' and 'statistical python package Stats Models' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The parameters of the hierarchical prior were set as follows: ντ = 100, ατ = 0.01, βτ = 1, αµ = 0.001, νµ = 100, λµ = 100. (Figure 2 caption), α0 = 0.015, the 2000 burn-in samples, the last 3000 MCMC samples, We adjusted the time discretization interval needed to get the spike counts and the order of the regression ( t = 0.1 msec and Q = 1, respectively). |