Nonlinear scaling of resource allocation in sensory bottlenecks
Authors: Laura Rose Edmondson, Alejandro Jimenez Rodriguez, Hannes P. Saal
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we show analytically and numerically that resource allocation scales nonlinearly in efficient coding models that maximize information transfer, when inputs arise from separate regions with different receptor densities. (...) We tested numerically how neurons in a visual bottleneck should be allocated to different input regions, reflecting the fact that the density of cone photoreceptors is not constant across the retina. |
| Researcher Affiliation | Academia | Laura R. Edmondson1,3, Alejandro Jiménez-Rodriguez2,3, Hannes P. Saal1,3 1Department of Psychology 2Department of Computer Science 3Sheffield Robotics The University of Sheffield {lredmondson1,a.jimenez-rodriguez,h.saal}@sheffield.ac.uk |
| Pseudocode | No | The paper provides mathematical derivations and descriptions of the methods, but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to a dataset used (http://salicon.net), but does not state that the code for their methodology is open-source or provide a link to it. |
| Open Datasets | Yes | We included 2,000 randomly sampled images from the SALICON image data set2 [13], converted the images to 8-bit grayscale, and then extracted luminance values along horizontal lines extending 160 pixels each. (...) 2The full data set can be downloaded from http://salicon.net. |
| Dataset Splits | No | The paper describes using a dataset but does not specify any training, validation, or test splits. It primarily focuses on analytical derivations and numerical simulations based on the properties of the dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to conduct the numerical simulations or analysis. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., programming languages, libraries, or specialized solvers). |
| Experiment Setup | No | The paper describes the mathematical derivations and steps for calculating eigenvalues from the data, but it does not specify experimental setup details such as hyperparameters, learning rates, batch sizes, or training schedules, which are typical for machine learning models. |