Imagine the brain as a powerful computer, but not just any computer. It’s like comparing a supercomputer to your old flip phone! Scientists are trying to understand how consciousness and neural computation are connected. They focus on the cerebral cortex, which is responsible for the features of consciousness like information processing and action initiation. This part of the brain has some fascinating characteristics that set it apart from other neural circuits. The cerebral cortex has incredible computational power, but it’s not just about the number of computing units. Each computing unit engages in noisy coding, allowing random events to influence outcomes. This results in synchronized firing of different groups of cells at various frequencies. As the system learns from observations, it becomes more ordered over time, encoding predictive value. And despite all this activity, the cerebral cortex is remarkably energy-efficient! The unique features of the cerebral cortex suggest that it engages in probabilistic computation, where probabilities play a role in how information is processed. By understanding these probabilistic mechanisms, scientists can shed light on how consciousness arises from cortical neural networks.

Abstract
A central problem in the cognitive sciences is identifying the link between consciousness and neural computation. The key features of consciousness—including the emergence of representative information content and the initiation of volitional action—are correlated with neural activity in the cerebral cortex, but not computational processes in spinal reflex circuits or classical computing architecture. To take a new approach toward considering the problem of consciousness, it may be worth re-examining some outstanding puzzles in neuroscience, focusing on differences between the cerebral cortex and spinal reflex circuits. First, the mammalian cerebral cortex exhibits exascale computational power, a feature that is not strictly correlated with the number of binary computational units; second, individual computational units engage in noisy coding, allowing random electrical events to gate signaling outcomes; third, this noisy coding results in the synchronous firing of statistically random populations of cells across the neural network, at a range of nested frequencies; fourth, the system grows into a more ordered state over time, as it encodes the predictive value gained through observation; and finally, the cerebral cortex is extraordinarily energy efficient, with very little free energy lost to entropy during the work of information processing. Here, I argue that each of these five key features suggest the mammalian brain engages in probabilistic computation. Indeed, by modeling the physical mechanisms of probabilistic computation, we may find a better way to explain the unique emergent features arising from cortical neural networks.

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