This repository provides the code for the framework presented in our exploration of interoceptive processing and cognitive models. We apply the RNN models proposed in this paper to model the behavioral dataset collected in this paper. We also explore how modifications can be made to the baseline architecture to account for interoceptive state (i.e. hunger).
Kim R., Li Y., & Sejnowski TJ. Simple Framework for Constructing Functional Spiking Recurrent Neural Networks. Proceedings of the National Academy of Sciences. 116: 22811-22820 (2019).
M. Ballestero-Arnau, B. Rodr ́ıguez-Herreros, N. Nu ̃no-Berm ́udez, and T. Cunillera. Sporadic fasting re- duces attentional control without altering overall executive function in a binary classification task. Physi- ology Behavior, 260:114065, 2023.
This code is forked from the implementation provided by Kim et al. in this repo. See USAGE.md for details on installation and usage.