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原文传递 Amygdala Modeling with Context and Motivation Using Spiking Neural Networks for Robotics Applications.

题名：	Amygdala Modeling with Context and Motivation Using Spiking Neural Networks for Robotics Applications.
作者：	Zelgen, M. A.
摘要：	Cognitive capabilities for robotic applications are furthered by developing an artificial amygdala that mimics biology. Theamygdala portion of the brain is commonly understood to control mood and behavior based upon sensory inputs, motivation, andcontext. This research builds upon prior work in creating artificial intelligence for robotics which focused on mood generatedactions. However, recent amygdala research suggests a void in greater functionality. This work developed a computational modelof an amygdala, integrated this model into a robot model, developed a comprehensive integration of the robot for simulation, andlive embodiment. The developed amygdala, instantiated in the Nengo Brain Maker environment, leveraged spiking neuralnetworks and the semantic pointer architecture to allow abstraction of neuron ensembles into high-level concept vocabularies. Testand validation were performed on a TurtleBot in both simulated (Gazebo) and live testing. Results were compared to a baselinemodel which has a simplistic, amygdala-like model. Metrics of nearest distance and nearest time were used for assessment. Theamygdala model is shown to outperform the baseline in both simulation, with a 70.8% improvement in nearest distance and, 4%improvement in nearest time, and real applications with a 62.4% improvement in nearest distance. Notably, this performanceoccurred despite a five-fold increase in architecture size and complexity.
总页数：	143 pages