An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building
Authors: Miaolong Yuan, Bo Tian, Vui Ann Shim, Huajin Tang, Haizhou Li
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments performed on a mobile robot show that cognitive maps of the real environment can be efficiently built. The proposed model would provide an alternative neuro-inspired approach for robotic mapping, navigation and localization. |
| Researcher Affiliation | Academia | 1Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632 2College of Computer Science, Sichuan University, Chengdu, China 610065 |
| Pseudocode | Yes | Algorithm 1: The Cognitive Map Building Algorithm |
| Open Source Code | No | The paper does not provide any concrete access to source code, nor does it state that the code will be made available. |
| Open Datasets | No | The paper describes experiments conducted in 'a large office environment of 35m 35m on a mobile robot,' which appears to be a custom experimental setup, and does not provide any information or links to publicly available datasets used for training. |
| Dataset Splits | No | The paper does not specify exact dataset split percentages or absolute sample counts for training, validation, or testing. |
| Hardware Specification | Yes | The robot consists of a Pioneer 3-DX mobile base, an RGB-D sensor and a laptop with an Intel(R) Core(TM) i7-3740 CPU with 16GB RAM. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers. |
| Experiment Setup | Yes | Table 1: Parameter Setting includes 'Shift in Outgoing Weights l 2', 'Size of Neural Sheet 40 40', 'Time-Constant of Neuron Response τ 10ms', 'Periodicity of the Formed Lattice λ [13, 21]', 'Gain gexc [1, 1.1]', 'Parameter B 0.5', 'Layers of Neural Sheet N 80', 'Learning Rate k 0.00005'. |