  Artificial Intelligence has historically relied on planning, heuristics, and
handcrafted approaches designed by experts. All the while claiming to pursue
the creation of Intelligence. This approach fails to acknowledge that
intelligence emerges from the dynamics within a complex system. Neurons in the
brain are governed by local rules, where no single neuron, or group of neurons,
coordinates or controls the others. This local structure gives rise to the
appropriate dynamics in which intelligence can emerge. Populations of neurons
must compete with their neighbors for resources, inhibition, and activity
representation. At the same time, they must cooperate, so the population and
organism can perform high-level functions. To this end, we introduce modeling
neurons as reinforcement learning agents. Where each neuron may be viewed as an
independent actor, trying to maximize its own self-interest. By framing
learning in this way, we open the door to an entirely new approach to building
intelligent systems.
