Some other aspects are pointed out, such as fireflies inspired models for communication networks, and the use of firefly synchronization concept in mobile networks and other devices. The response to synchronization observed in some firefly species is illustrated utilizing a model based on electronic fireflies, and we explore the consequences of the firefly courtship as a whole process including the males’ synchronization and the corresponding females’ response. In this chapter, we explain biological aspects related to fireflies flashing and their functionality. During a long time, this collective behavior was not recognized and validated as synchronous, but nowadays it constitutes a paradigmatic example of synchronization. Synchronous flashing in Fireflies is perhaps the first observed natural phenomenon displaying synchronization of a large ensemble. This node-centric network congestion estimation method considering average spatio-temporal scale will be widely used because of its simplicity and universality. Based on the widely used simulation platform NetLogo, the simulation results have proved the reasonability of the proposed estimation method by the stable values of the fixed traffic network intersections, which becomes increasingly stable as time goes on, and it is found that the node-centric values calculated by our estimation method is more stable than the values by the edge-centric because of its superiority. In this paper, we propose a node-centric network congestion method, which can evaluate the average spatio-temporal congestion scale around nodes. An estimation method to describe the average congestion around intersections or nodes is also valuable and needed to discover the most congested parts but is absent according to our literature researches. However, congestion is always node-centric and formed around intersections gradually. Many researchers have used various estimation methods based on the load data of roads in a single moment. a Spikes emitted by input neurons C and U reaching the synapse with postsynaptic motoneuron M at times tcf\documentclass triggering an action potential at the postsynaptic motoneuron MĬongestion estimation is a significant issue to analyse and mitigate network congestion. The setup of the experiment and its outcomes are described in this work.īasic associative topology. 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.
NETLOGO FOREACH SOFTWARE
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.
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