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GECCO
2004
Springer

An Evolutionary Autonomous Agent with Visual Cortex and Recurrent Spiking Columnar Neural Network

4 years 3 months ago
An Evolutionary Autonomous Agent with Visual Cortex and Recurrent Spiking Columnar Neural Network
Spiking neural networks are computationally more powerful than conventional artificial neural networks. Although this fact should make them especially desirable for use in evolutionary autonomous agent research, several factors have limited their application. This work demonstrates an evolutionary agent with a sizeable recurrent spiking neural network containing a biologically motivated columnar visual cortex. This model is instantiated in spiking neural network simulation software and challenged with a dynamic image recognition and memory task. Since the resulting agent or “virtual organism” initially contains many randomly and recurrently connected neurons, we use a genetic algorithm to evolve generations of this brain model that instinctively perform progressively better on the task. Our investigations lay the foundation for further experiments to resolve the question of whether autonomous agents with spiking neural networks can take advantage of the proven theoretical computat...
Rich Drewes, James B. Maciokas, Sushil J. Louis, P
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where GECCO
Authors Rich Drewes, James B. Maciokas, Sushil J. Louis, Philip H. Goodman
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