Approximating the Effect of Consciousness on Stochastic Brain Structures

Abstract

There is a large amount of evidence in the Parapsychology literature that indicates that consciousness is not an emergent property of neuronal interactions and can exist and function independently of a brain. Here we examine mathematical methods that can be used to derive dynamic equations for the mind-matter interactions occurring in the brain that these observations imply the existence of. We use the moments method to approximate the effect that consciousness has on stochastic binary decision-making neural networks, which we model using biologically realistic Wilson-Cowan equations (Deco et al., 2007). We show that small changes in the variance of the randomness (on the order of 0.1 Hz²) consumed by the neural networks can bias the networks to select one binary decision value over the other. Using observations about the interconnectedness of relatively isolated groups of neurons, we argue that biases of the size predicted by the approximation would be sufficient to allow a brain-independent consciousness to exert significant control over the brain. The results that we present here are relevant to any theory positing that rote computations carried out by neurons are not the sole contributor to consciousness.