AI-SIMCOG: a simulator for spiking neurons and multiple animats' behaviours
Designing a biologically inspired neural architecture as a controller for a complete animat or physical robot environment, to test the hypotheses on intelligence or cognition is non-trivial, particularly, if the controller is a network of spiking neurons. As a result, simulators that integrate spike coding and artificial or real-world platforms are scarce. In this paper, we present artificial intelligence simulator of cognition, a software simulator designed to explore the computational power of pulsed coding at the level of small cognitive systems. Our focus is on convivial graphical user interface, real-time operation and multilevel Hebbian synaptic adaptation, accomplished through a set of non-linear dynamic weights and on-line, life-long modulation. Inclusions of transducer and hormone components, intrinsic oscillator and several learning functions in a discrete spiking neural algorithm are distinctive features of the software. Additional features are the easy link between the production of specific neural architectures and an artificial 2D-world simulator, where one or more animats implement an input-output transfer function in real-time, as do robots in the real world. As a result, the simulator code is exportable to a robot's microprocessor. This realistic neural model is thus amenable to investigate several time related cognitive problems.
simulator robot artificial intelligence spiking neurons model learning and memory dynamical synapse