Efficient Neural Codes under Metabolic Constraints
Authors: Zhuo Wang, Xue-Xin Wei, Alan A. Stocker, Daniel D. Lee
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here we formulate a coding framework which explicitly deals with noise and the metabolic costs associated with the neural representation of information, and analytically derive the optimal neural code for monotonic response functions and arbitrary stimulus distributions. |
| Researcher Affiliation | Academia | Zhuo Wang Department of Mathematics University of Pennsylvania, Xue-Xin Wei Department of Psychology University of Pennsylvania, Alan A. Stocker Department of Psychology University of Pennsylvania, Daniel D. Lee Department of Electrical and System Engineering University of Pennsylvania |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about open-source code availability or links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not describe experiments performed on any publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not mention specific software dependencies or versions. |
| Experiment Setup | No | The paper describes theoretical work and does not detail any experimental setup or hyperparameters. |