Information Geometry of the Retinal Representation Manifold
Authors: Xuehao Ding, Dongsoo Lee, Joshua Melander, George Sivulka, Surya Ganguli, Stephen Baccus
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To model the joint probability distribution of neural responses conditioned on the stimulus, we created a stochastic encoding model of a population of salamander retinal ganglion cells based on a three-layer convolutional neural network model. This model not only accurately captured the mean response to natural scenes but also a variety of second-order statistics. With the model and the proposed theory, we computed the Fisher information metric over stimuli to study the most discriminable stimulus directions. |
| Researcher Affiliation | Academia | 1,5Department of Applied Physics, Stanford University 2,3Neurosciences Phd Program, Stanford University 4Department of Electrical Engineering, Stanford University 6Department of Neurobiology, Stanford University {xhding, dsnl, melander, gsivulka, sganguli, baccus}@stanford.edu |
| Pseudocode | No | The paper includes a diagram of the model architecture (Figure 2) and describes the methods in paragraph form, but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to source code repositories or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | Yes | The deterministic part of the model including its hyperparameters and training method is adopted from Ref. [12, 13, 14] |
| Dataset Splits | No | The paper mentions data optimization and evaluation, including a 'test set' for results, but it does not specify explicit training, validation, and test dataset splits with percentages, sample counts, or specific predefined split methodologies. |
| Hardware Specification | Yes | The optimization was performed using ADAM [25] via Pytorch [26] on NVIDIA TITAN Xp, Ge Force GTX TITAN X, Ge Force RTX 3090, TITAN RTX, TITAN V, and Ge Force RTX 2080 Ti GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not provide a specific version number. No other software components are listed with version numbers. |
| Experiment Setup | Yes | CNN parameters and one-hot parameters {wk} were optimized together with the standard Poisson loss function and Lone-hot to fit mean firing rates smoothed using a 10 ms Gaussian filter. The optimization was performed using ADAM [25]... The network was regularized with an L2 weight penalty at each layer and an L1 penalty on the model output. |