On information representation in the brain

A complex nervous system must transmit information many times, potentially over relatively vast distances. For example, information in vision originates from the retina, conveyed via the optic nerve to the Lateral Geniculate Nucleus (LGN) before arriving at the visual cortex at the back of the brain – a distance spanning almost the entire length of the brain. From there, some information may be further transmitted forward onto the prefrontal cortex at the front of the brain. How does the brain attain a high reliability (i.e. minimal errors) throughout the entirety of such a communications process? Is information in the brain represented continuously, or discretely? A communications systems engineer, such as Claude Elwood Shannon, would stipulate that a continuous neural coding protocol is too error prone due to noise. To attain high reliability, a discrete neural coding protocol would be the necessary pre-requisite. This is the conclusion of my work in Chapter 2, based on a theoretical simulation of information transmission (i.e. communications) between neurons. My analysis of behavioral tasks in Chapters 3 (i.e. a conjunction probability task) and 4 (i.e. an intertemporal choice task) further reinforced this conclusion – that, information in the brain is most likely to be represented discretely. The right question to pose is not one about continuous-versus-discrete representation, but rather, one that is focused on how fine-grained the discreteness is (i.e. how many bits of precision). We cannot and should not simply assume the use of continuous models in modeling cognitive tasks – we need to test how fine grained the discreteness is. This is a major advance and demarcation from the continuous model assumption typically employed in data analysis