Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability
Authors: David Liu, Mate Lengyel
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | After systematically validating our method on synthetic data, we apply it to two foundational datasets of animal navigation: head direction cells in freely moving mice and hippocampal place cells in rats running along a linear track. Our model exhibits competitive or better predictive power compared to state-of-the-art baselines, and outperforms them in terms of capturing interspike interval statistics. |
| Researcher Affiliation | Academia | David Liu Department of Engineering University of Cambridge dl543@cam.ac.uk Máté Lengyel Department of Engineering University of Cambridge Department of Cognitive Science Central European University m.lengyel@eng.cam.ac.uk |
| Pseudocode | No | The paper describes the generative model and inference scheme but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide a JAX [4] implementation of our method as well as established baseline models within a scalable general variational inference scheme. Code available at https://github.com/davindicode/nonparametric-nonrenewal-process |
| Open Datasets | Yes | We apply it to two foundational datasets of animal navigation: head direction cells in freely moving mice [63, 64] and hippocampal place cells in rats running along a linear track [54]. |
| Dataset Splits | Yes | Experiments involve fitting to the first half of a dataset (≈18 min. for mouse, ≈32 min. for rat), and testing on the second half split into 5 consecutive segments. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions a "JAX [4] implementation" but does not specify its version or other software dependencies with their respective version numbers. |
| Experiment Setup | Yes | All datasets discretize spike trains and input time series at regular intervals of Δt = 1 ms. We use a product kernel for k(x, x') with periodic kernels for angular dimensions, and squared exponential kernels in other cases. For k(τ̃, τ̃') and k(ξ̃, ξ̃'), we pick a product kernel with Matérn-3/2 (see Fig. 12 for different kernel choices) and set the maximum ISI lag K = 3. [...] GP inducing points were randomly initialized, and for a fair comparison, all models used 8 inducing points for each covariate dimension (including temporal dimensions τ̃ and ξ̃ in the NPNR process). For each experiment, we repeat model fitting with 3 different random seeds and pick the model with the best training likelihood. Further details on experiments are presented in Appendix C. |