Software

Available on GITHUB

Authors: Thiago Mosqueiro

To examine the impact of individual variations in foraging behaviors, we developed a spatially-explicit Agent-Based Model, ABBAS (Animal Behavior Based on Agents Simulations). It implements a detailed flight dynamics based on stochastic diffusion processes, parametrized following a wide range of experiments (see below for more details), and a recruitment mechanism that mimics the waggle dance in honey bees.

Please cite: Task allocation and site fidelity jointly influence foraging regulation in honey bee colonies. Royal Society Open Science 2017.

Available on GITHUB

Authors: Flavia Huerta, Ramon Huerta, Thiago Mosqueiro, Jordi Fonollosa, Nikolai Rulkov, Irene Rodriguez-Lujan

This github repository contains a an example of how to load and classify data from an array of chemical sensors. In this particular case, the idea is to distinguish events with banana and wine in an uncontrolled environment.

Please cite: Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring. Chemometrics and Intelligent Laboratory Systems 2016.

Available on GITHUB

Authors: Thiago Mosqueiro, Leonardo P. Maia

You will find an implementation of the probabilistic Greenberg-Hastings automaton, which is a formal neural model that relies on an abstraction of the neural dynamics in discrete states. With this implementaiton, we investigated neuronal avalanches and emergence of critical dynamics. Although memory-consuming, this algorithm was optimized for fast computation time. I am not just sharing the core code, but also a small example script to reproduce one of the figures shown in this paper. You can find this example in Example folder.

Please cite: Optimal Channel Efficiency in a Sensory Network. Physical Review E 2013.