“Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation
Authors: Wen-Hao Zhang, He Wang, K. Y. Michael Wong, Si Wu
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Mimicking the experimental protocol, our model reproduces the characteristics of congruent and opposite neurons, and demonstrates that in each module, the sisters of congruent and opposite neurons can jointly achieve optimal multisensory information integration and segregation. We first verify that our model reproduces the characteristics of congruent and opposite neurons. Figs. 4A&B show the tuning curves of a congruent and an opposite neuron with respect to either visual or vestibular cues, which demonstrate that neurons in our model indeed exhibit the congruent or opposite direction selectivity similar to Fig. 1. Finally, to verify whether Bayes-optimal multisensory information processing is achieved in our model, we check the validity of Eqs. (7-8) for multisensory integration p(sm|x1, x2) by congruent neurons in module m, and Eqs. (12-13) for multisensory segregation D(sm|xm; sm|xl) (l = m) by opposite neurons in module m. |
| Researcher Affiliation | Academia | 1Department of Physics, Hong Kong University of Science and Technology, Hong Kong. 2State Key Lab of Cognitive Neuroscience and Learning, and IDG/Mc Govern Institute for Brain Research, Beijing Normal University, China. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper describes a computational model that reproduces characteristics observed in experimental data from cited works (e.g., Fig. 1 (A-B) adapted from [1], (C) from [13]). It does not mention training on a specific publicly available dataset with concrete access information for replication. |
| Dataset Splits | No | The paper describes a computational model and its dynamics, and validates its output against theoretical predictions and existing experimental observations. It does not mention training, validation, or test dataset splits in the context of machine learning model training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | Parameters: Jr = 0.4 J, Jc = Jo [0.1, 0.5]Jr, α1 = α2 [0.8, 1.6]U 0 m, Ib = 1, F = 0.5. |