Sequential behavior and learning in evolved dynamical neural networks
Adaptive Behavior
Spikes: exploring the neural code
Spikes: exploring the neural code
Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots
ER '01 Proceedings of the International Symposium on Evolutionary Robotics From Intelligent Robotics to Artificial Life
The evidence for neural information processing with precise spike-times: A survey
Natural Computing: an international journal
Evolution of Neural Architecture Fitting Environmental Dynamics
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Sequential behavior has been the subject of numerous studies that involve agent simulations. In such research, investigators often develop and examine neural networks that attempt to produce a sequence of outputs. Results have provided important insights into neural network designs but they offer a limited understanding of the underlying neural mechanisms. It is therefore still unclear how relevant neural parameters can advantageously be employed to alter motor output throughout a sequence of behavior. Here we implement a biologically based spiking neural network for different sequential tasks and investigate some of the neural mechanisms involved. It is demonstrated how a genetic algorithm can be employed to successfully evolve a range of neural parameters for different sequential tasks.