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Neural Symbolic Decision Making: A Scalable and Realistic Foundation for Cognitive Architectures

We have developed a computational model using spiking neurons that provides the decision-making capabilities required for production system models of cognition. This model conforms to the anatomy and connectivity of the basal ganglia, and the neuron parameters are set based on known neurophysiology. Behavioral-level timing and neural-level spike predictions have been made, and are consistent with empirical results. Here we demonstrate how this system can be used to implement standard production system rules, including complex variable matching and other binding operations. This results in predictions about neural connectivity in the thalamus and cortex. We believe our model can be used as a part of any biologically inspired cognitive architecture, allowing researchers to connect low-level neural implementation details to high-level behavioral effects.