The brain creates its own internal representations of time and space, which may differ from external time and space. This work presents a mechanistic model of interconnected processes that encode phenomenal representations of space and time, elaborating how these processes bind together. A stochastic version of the model explores how binding strength interacts with noise, using spectral entropy to characterize noise effects. Results show that spectral entropy values for strongly bound systems are similar to those for weakly bound or decoupled systems, indicating that the binding mechanism is resilient to noise.
Random fluctuations in neuronal processes may contribute to variability in perception and increase information capacity in neuronal networks. This paper develops a stochastic model to examine how noise affects dynamical systems that mimic neural correlates of consciousness. Power spectral densities and spectral entropy values were computed for systems with varying numbers of mutually connected processes. Spectral entropy decreased linearly as the number of processes doubled, and power spectral density frequencies shifted to higher values with increasing system size, indicating a greater impact of negative feedback loops and regulation in larger systems. The results suggest that large dynamical systems of mutually connected and negatively regulated processes are more robust against inherent noise than small systems.
A mathematical framework is proposed in which a system of mutually connected processes is isomorphic to a conscious percept of a point in space. In this system, any process can be derived through all other processes that form its complement. A dynamical system of oscillators is crafted to preserve the mutual relationships among processes, creating an operational map isomorphic to a distance matrix that mimics space-like properties. This approach provides a novel way to analyze neural-like oscillatory dynamics to extract information relevant to specific conscious percepts, potentially aiding the search for neural correlates of consciousness.