The duality of sensory inference and optimal control was known since 1960s and has recently been recognized as common computations required for  posterior distributions in dynamic Bayesian inference and  value functions in optimal control. Meanwhile, an intriguing question about the brain is why  entire neocortex shares a canonical six-layer architecture , while its posterior and anterior halves are engaged in sensory processing and motor control, respectively. Here we consider a hypothesis that the anterior and posterior cortical circuits evolved for dual computations for sensory inference and optimal control, or perceptual and value-based decision making, respectively. We  explore how different types of cortical neurons may represent different variables , such as prior and posterior distributions and value functions, and what cortical dynamics may realize required computations. We further discuss experimental and computational approaches are required for scrutinizing this dual cortical circuit hypothesis .
The duality of sensory inference and motor control has been known since the 1960s and has recently been recognized as the commonality in computations required for the posterior distributions in Bayesian inference and the value functions in optimal control. Meanwhile, an intriguing question about the brain is why the entire neocortex shares a canonical six-layer architecture while its posterior and anterior halves are engaged in sensory processing and motor control, respectively. Here we consider the hypothesis that the sensory and motor cortical circuits implement the dual computations for Bayesian inference and optimal control, or perceptual and value-based decision making, respectively. We first review the classic duality of inference and control in linear quadratic systems and then review the correspondence between dynamic Bayesian inference and optimal control. Based on the architecture of the canonical cortical circuit, we explore how different cortical neurons may represent variables and implement computations .
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optimal control was known since
motor control has been known since the
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common
the commonality in
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the
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dynamic
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the
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the
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,
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the
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anterior and posterior cortical circuits evolved for
sensory and motor cortical circuits implement the
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sensory
Bayesian
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first review the classic duality of inference and control in linear quadratic systems and then review the correspondence between dynamic Bayesian inference and optimal control. Based on the architecture of the canonical cortical circuit, we
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types of
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different variables , such as prior and posterior distributions and value functions, and what cortical dynamics may realize required computations. We further discuss experimental and computational approaches are required for scrutinizing this dual cortical circuit hypothesis
variables and implement computations