Consciousness, Exascale Computational Power, Probabilistic Outcomes, and Energetic Efficiency

Consciousness may arise from the brain's use of probabilistic computation, a new approach to an old problem. The cerebral cortex, unlike spinal circuits or classical computers, shows five key features: exascale computing power not tied to binary unit count; noisy coding where random electrical events influence signaling; synchronous firing of random cell populations at nested frequencies; growth toward order by encoding predictive value; and extraordinary energy efficiency with little entropy loss. These features suggest the mammalian brain engages in probabilistic computation, which could explain the unique emergent properties of cortical networks.

Study at a glance

Abstract

AbstractA 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.