Scaling the Poisson GLM to massive neural datasets through polynomial approximations
Authors: David Zoltowski, Jonathan W. Pillow
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | After validating these estimators on simulated spike train data and a spike train recording from a primate retinal ganglion cell, we demonstrate the scalability of these methods by fitting a fully-coupled GLM to the responses of 831 neurons recorded across five different regions of the mouse brain. |
| Researcher Affiliation | Academia | David M. Zoltowski Princeton Neuroscience Institute Princeton University; Princeton, NJ 08544 zoltowski@princeton.edu. Jonathan W. Pillow Princeton Neuroscience Institute & Psychology Princeton University; Princeton, NJ 08544 pillow@prince ton.edu |
| Pseudocode | No | The paper describes methods and derivations but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | An implementation of pa GLM is available at https://github.com/davidzoltowski/paglm. |
| Open Datasets | Yes | We next tested the pa GLM-2 estimator using spike train data recorded from a single parasol retinal ganglion cell (RGC) in response to a full field binary flicker stimulus binned at 8.66 ms [24]. We fit a fully-coupled Poisson GLM to the spiking responses of N = 831 neurons simultaneously recorded from the mouse thalamus, visual cortex, hippocampus, striatum, and motor cortex using two Neuropixels probes [8]. |
| Dataset Splits | Yes | We held out the first minute as a validation set and used the next 10 minutes to compute the exact and pa GLM-2 MAP estimates with a fixed ridge prior, as hyperparameter optimization was computationally infeasible in the exact MAP case. The fit model had positive spike prediction accuracy for 79.6% of neurons (469 out of 589) whose firing rates were greater than 0.5 Hz in both the training and validation periods. |
| Hardware Specification | No | The paper mentions data was recorded using 'Neuropixels probes' [8] but does not provide specific details about the hardware (e.g., GPU/CPU models, RAM) used to perform the computational experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | In our experiments, we implement ridge regression with C 1 = λI, Bayesian smoothing with C 1 = λL where L is the discrete Laplacian operator [16, 14], and automatic relevance determination (ARD) with C 1 ii = λi [10, 22, 18, 25]. We used ridge regression to regularize the weights. We selected the approximation interval using a random subset of the data and optimized the ridge penalty by optimizing the approximate loglikelihood (18). We used a random subset of the training data to select the approximation interval for each neuron and we computed the exact MAP estimates using 50 iterations of quasi-Newton optimization. We placed an ARD prior over each set of 3 coupling weights incoming from other neurons, and optimized the ARD hyperparameters using the fixed-point update equations [2, 18]. We found that sometimes this method under-regularized and therefore we thresholded the prior precisions values from below at 26. |