Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks
Authors: Xingsi Dong, Tianhao Chu, Tiejun Huang, Zilong Ji, Si Wu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have included the code for reproducing the main results in the supplemental material. |
| Researcher Affiliation | Academia | 1, School of Psychology and Cognitive Sciences, Peking Univerisity. 2, IDG/Mc Govern Institute for Brain Research, Peking University. 3, PKU-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University. 4, School of Electronics Engineering and Computer Science, Peking University. |
| Pseudocode | No | The paper describes mathematical models and equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have included the code for reproducing the main results in the supplemental material. |
| Open Datasets | No | The paper mentions and models phenomena observed in human free memory retrieval based on a previous study [11]. However, it does not state that it uses a publicly available dataset for its *own* experiments or provides access information for such a dataset. |
| Dataset Splits | No | The paper describes theoretical analyses and simulations but does not specify dataset splits (e.g., training, validation, test) in the context of typical data-driven experiments. |
| Hardware Specification | No | We do not use GPU or CPU clusters. It is sufficient to run our code on a laptop. This statement indicates that specific hardware (like GPUs/CPUs) was not a focus, and does not provide any specific model numbers or detailed specifications of the 'laptop' used. |
| Software Dependencies | No | The paper does not list specific software names with version numbers for reproducibility (e.g., Python, PyTorch, or other libraries/solvers with their versions). |
| Experiment Setup | No | The paper states 'For the setting of hyperparameters, please see SI.4 in the Supplementary Information.' indicating that these details are not in the main text of the paper. |