Graduate (Archived)
Major
Applied Engineering (M.S.)
Research Presentation Abstract
In this paper, simulation of the brain based on an artificial spiking neuron model is used to create a self-learning algorithm. The spiking neuron simulation is used to demonstrate a neuromodulation program in which the reward seeking properties of dopamine, the risk-adverse effects of serotonin, and the attention-focusing effects of the cholinergic and noradrenergic systems are applied to a mobile robotic platform as it moves autonomously throughout an environment. External stimuli is recorded by the program as spiking “events” that result in corresponding amounts of dopamine and serotonin influenced spiking patterns. These spiking patterns affect how the robot adapts to its surroundings depending on what type of “mood” is set in the internal programming. Also, alternate hardware platforms are analyzed to see how the neural model can be expanded to possibly include cloud computing as a method of control.
Keywords
adaptive behavior, bio-inspired control, computational neuroscience, neuromodulation, neural networking, neurorobotics, Robot Operating System, ROS
Publication Type and Release Option
Presentation (Open Access)
Copyright
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Principal Faculty Mentor
Biswanath Samanta
Principal Faculty Mentor Email
bsamanta@georgiasouthern.edu
Principal Faculty Mentor’s Department
Mechanical Engineering (CEIT)
Recommended Citation
Muhammad, Cameron, "Applying Spiking Neural Network Simulation to Neuromodulatory Autonomous Robot Control" (2014). Phi Kappa Phi Research Symposium (Archived). 3.
https://digitalcommons.georgiasouthern.edu/pkp/2014/Graduate/3
Archived Content
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Included in
Acoustics, Dynamics, and Controls Commons, Bioelectrical and Neuroengineering Commons, Controls and Control Theory Commons, Dynamics and Dynamical Systems Commons, Electro-Mechanical Systems Commons, Systems and Communications Commons
Applying Spiking Neural Network Simulation to Neuromodulatory Autonomous Robot Control
In this paper, simulation of the brain based on an artificial spiking neuron model is used to create a self-learning algorithm. The spiking neuron simulation is used to demonstrate a neuromodulation program in which the reward seeking properties of dopamine, the risk-adverse effects of serotonin, and the attention-focusing effects of the cholinergic and noradrenergic systems are applied to a mobile robotic platform as it moves autonomously throughout an environment. External stimuli is recorded by the program as spiking “events” that result in corresponding amounts of dopamine and serotonin influenced spiking patterns. These spiking patterns affect how the robot adapts to its surroundings depending on what type of “mood” is set in the internal programming. Also, alternate hardware platforms are analyzed to see how the neural model can be expanded to possibly include cloud computing as a method of control.