Computational modeling of emotion: explorations through the anatomy and physiology of fear conditioning
Recent discoveries about the neural system and cellular mechanisms in pathways mediating classical fear conditioning have provided a foundation for pursuing concurrent connectionist models of this form of emotional learning. The models described are constrained by the known anatomy underlying the behavior being simulated. To date, implementations capture salient features of fear learning, both at the level of behavior and at the level of single cells, and additionally make use of generic biophysical constraints to mimic fundamental excitatory and inhibitory transmission properties. Owing to the modular nature of the systems model, biophysical modeling can be carried out in a single region, in this case the amygdala. Future directions include application of the biophysical model to questions about temporal summation in the two sensory input paths to amygdala, and modeling of an attentional interrupt signal that will extend the emotional processing model to interactions with cognitive systems.