Neural noise and suppression in visual processing

Abstract

Signal transduction in sensory systems is affected by two major neural mechanisms: neural noise and suppression. Both of these factors present limits on the perceptual abilities of the observer. For example, in contrast discrimination both elevate thresholds. Suppression and neural noise have been implicated in normal sensory development, ageing and several neurological disorders. Of particular interest are autism spectrum conditions (ASCs), in which both neural noise and suppressive mechanisms seem to be atypical. This thesis addresses several issues surrounding the measurement and neural implications of neural noise and suppression. Firstly, it investigates where in the brain neural noise affects sensory processing. Using machine learning algorithms to analyse electro- and magneto-encephalography data, it was found that the main source of neural noise is early sensory cortex. Secondly, it compares psychophysical paradigms used to dissociate the effects of noise and suppression, and suggests refined methods, in particular, using double-pass consistency. Thirdly, it investigates the neural effects of modulating neural noise and suppression selectively using transcranial magnetic stimulation (TMS). It reveals that two existing TMS protocols are suitable for this: single pulses suppress neural signals, whereas triple-pulse TMS increases neural noise. Lastly, the thesis investigates neural noise and gain control (a suppressive mechanism) in ASC. The findings show a relationship between sensory noise and autistic traits in the neurotypical population. Furthermore, electrophysiology data from ASC children and adults as well as a genetic Drosophila model of autism revealed a deficit in the transient dynamics of ASC visual systems, which changes over the course of development. Striking similarities between the fruit fly (Nhe3) model and humans suggests that the genetic model is suitable for further research on ASC sensory symptoms. Taken together, this thesis expands the understanding of neural noise and suppression as well as the situations in which these mechanisms are implicated