![]() Conversely, conventional environmental models can benefit from an agent-based depiction of the feedbacks and heuristics which influence the decisions of groundwater users. For instance, the agent-based models which are increasingly popular for groundwater management studies can be made more useful by directly accounting for the hydrological processes which drive environmental outcomes. However, such models should acknowledge the complexity and uncertainty of both of the underlying subsystems. The quantitative modelling of social-ecological systems can provide useful insights into the interplay between social and environmental processes, and their impact on emergent system dynamics. The setup of the experiment and its outcomes are described in this work.Ī Coupled Simulation Architecture for Agent-Based/Geohydrological Modelling The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi- agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley- based and multi-compartmental models. Jimenez-Romero, Cristian Johnson, Jeffrey SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.
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