Computational neural modelling of bottom-up information and top-down attention in auditory perception

Project description

The primary aim of this project is to advance our understanding of how the brain processes auditory information and how it can make sense of acoustic signals that are often mixed with other sounds in frequency and time, depending on the current behavioural or perceptual needs. In particular, we aim to investigate the processing strategies used in the auditory cortex to enhance the perception of relevant sounds in the presence of background noise and distractors. We will develop neuronal network models that will be used to elucidate the mechanisms by which attention and plasticity modify neuronal responses in a task-dependent fashion. The models will be constrained by electrophysiological and behavioural data recorded some of the world’s top experimentalists. This understanding is likely to be relevant for other sensory modalities.

A better understanding of the impact of top-down processes on perception through a computational approach will be a significant step forward in neuroscience. The outcomes of this research will yield new insights into information processing in the brain, which is of interest to neuroscience research in general and to those working on brain-inspired computation. In addition, the procedures used will offer valuable results concerning the development, optimisation, simulation and analysis of large-scale spiking neural network models.

Project team

Leader: David Grayden

Staff: David Grayden

Collaborators: Shihab Shamma (University of Maryland), Johnathan Fritz (University of Maryland)

Sponsors: Australian Research Council

Other projects

Convergence of engineering and IT with the life sciences projects

Research Centre

Neuroengineering Research Laboratory

Disciplines

Biomedical Engineering,Electrical & Electronic Engineering

Domains

Convergence of engineering and IT with the life sciences

Keywords

auditory processing; biosignals; computational neuroscience; neural models; neuroengineering