Unlocking neural population non-stationarities using hierarchical dynamics models
Authors: Mijung Park, Gergo Bohner, Jakob H. Macke
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On neural population recordings from primary visual cortex, we demonstrate that our model provides a better account of the structure of neural firing than stationary dynamics models. Simulated data: We first illustrate the performance of N-PLDS on a simulated population recording from 40 neurons consisting of 100 trials of length T = 200 time steps each. Neurophysiological data: How big are non-stationarities in neural population recordings, and can our model successfully capture them? To address these questions, we analyzed a population recording from anaesthetized macaque primary visual cortex consisting of 64 neurons stimulated by sine grating stimuli. |
| Researcher Affiliation | Academia | Mijung Park1, Gergo Bohner1, Jakob H. Macke2 1 Gatsby Computational Neuroscience Unit, University College London 2 Research Center caesar, an associate of the Max Planck Society, Bonn Max Planck Institute for Biological Cybernetics, Bernstein Center for Computational Neuroscience T ubingen {mijung, gbohner}@gatsby.ucl.ac.uk, jakob.macke@caesar.de |
| Pseudocode | No | The paper describes the algorithmic steps for Bayesian Laplace propagation, but it does so in paragraph form with mathematical equations, not in a structured pseudocode block or a clearly labeled algorithm. |
| Open Source Code | Yes | Code available at http://www.mackelab.org/code. |
| Open Datasets | Yes | To address these questions, we analyzed a population recording from anaesthetized macaque primary visual cortex consisting of 64 neurons stimulated by sine grating stimuli. The details of data collection are described in [5], but our data-set also included units not used in the original study. We binned the spikes recorded during 100 trials of length 4s (stimulus was on for 2s) of the same orientation using 50ms bins, resulting in trials of length T = 80 bins. We thank Alexander Ecker and the lab of Andreas Tolias for sharing their data with us [5] (see http://toliaslab.org/publications/ecker-et-al-2014/). |
| Dataset Splits | Yes | We used 10-fold cross validation to evaluate performance of the model, i.e. repeatedly divided the data into test data (10 trials) and training data (the remaining 90 trials). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU/GPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | We used a 4-dimensional latent state and assumed that the population consisted of two homogeneous subpopulations of size 20 each, with one modulatory input controlling rate fluctuations in each group. We used 10-fold cross validation to evaluate performance of the model, i.e. repeatedly divided the data into test data (10 trials) and training data (the remaining 90 trials). We tested the model with different latent dimensionalities ranging from k = 1 to k = 8. |