Slug City is an integrative physiology lab studying how complexity in brain and behavior appeared in evolution. We describe the neural mechanisms for how animals organize their behavior in terms of sensation, motivation, and experience, and how the basic template for economic decision is embellished in evolution for enhanced cognition. We analyze decision-making in behavioral expression and the underlying neural network circuitry. In addition, we analyze interactions of neural networks and the regulation of network activity by cell signaling pathways involving neuromodulatory messengers and second messenger pathways. We use the simple carnivorous marine snail Pleurobranchaea californica and the greatly more complex octopus as model systems, taking advantage of similarities and differences in comparative analyses of behaviors and brains underlying their successes as generalized predators. Neurocomputational models emerge from the behavioral and physiological work in software and hardware platforms for autonomous cost-benefit decision-making and robotics.
Our Principal Investigator is Rhanor Gillette.
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Contemporary artificial intelligence lacks the attributes of natural intelligence, in particular, the ability to relate information affectively. Accordingly, it is notable that the most complex animal behaviors serve primitive homeostatic goals, and emerge from the primitive mechanisms generating motivation and reward learning. Here we show in simulation the function of a basic neuronal circuit for cost-benefit decisions, derived from studies of a predatory generalist, the sea-slug Pleurobranchaea californica, and based on the effective integration of information. Its simplicity may reflect distant ancestral qualities on which complexities in economic, cognitive, and social behaviors were built. The simulation validates experimental data and provides a basic module for the expansion of behavioral complexity.