Abstract
AbstractWe propose that the neocortex implements active predictive coding (APC), a form of predictive coding that incorporates hierarchical dynamics and actions. In this model, each neocortical area estimates both sensory states and actions, and the cortex as whole learns to predict the sensory consequences of actions at multiple hierarchical levels. “Higher” cortical areas maintain more abstract representations at larger spatiotemporal scales compared to “lower” areas. Feedback from higher areas modulate the dynamics of both state and action networks in lower areas. This allows the cortical network to model the complex dynamics and physics of the world in terms of simpler compositional elements (state transition functions). Simultaneously, current higher level goals invoke sequences of lower level sub-goals and actions, allowing the network to solve complex planning problems by composing simpler solutions. Planning (“system 2” thinking) in turns allows the network to learn, over time, perception-to-action mappings (policies; “system 1” thinking) at multiple abstraction levels. We provide examples from simulations illustrating how the same APC architecture can solve problems that, at first blush, seem very different from each other: (1) how do we recognize an object and its parts using eye movements? (2) why does perception seem stable despite eye movements? (3) how do we learn compositional representations, e.g., part-whole hierarchies, and nested reference frames for equivariant vision? (4) how do we model the “physics” of a complex environment by decomposing it into simpler components? (5) how do we plan actions in a complex domain to achieve a goal by composing sequences of sub-goals and simpler actions? and (6) how do we form episodic memories of sensory-motor experiences? We propose a mapping of the APC network to the laminar architecture of the cortex and suggest possible roles for cortico-cortical, cortico-thalamic, cortico-hippocampal and cortico-subcortical pathways.
Publisher
Cold Spring Harbor Laboratory
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