Developing a neurocognitive model of sound and speech recognition
Mice are an ideal candidate organism for studying neural mechanisms underlying vocal communication. They emit ultrasonic vocalisations in social contexts and the genetic basis of these signals can provide insight into disorders affecting verbal communication such as Autism Spectrum Disorder (ASD). There is evidence to suggest that the shape or waveform pattern of the call influences behaviour and may more accurately represent differences in communication. The major challenge for quantitatively assessing differences in vocalisations lies in the quantity and complexity of data that must be analysed. This project will enable the design and validation of algorithms for automatic classification of mouse ultrasonic vocalisations, and will represent a first attempt to do so with this level of sophistication and scope. An earlier version of the algorithm applied to human speech successfully classified altered speech prosody in ASD patients. For this study, the algorithm will be adapted to the temporal, frequency and amplitude resolution of mouse hearing.
Our goals are:
- Develop and validate a classification algorithm.
- Conduct objective and high throughput analysis to investigate the temporal organisation of calls, studying the sequences of call types to detect any non-random sequences within ultrasonic emissions.
- Investigate if sequences of call types are perceived at a behavioural level utilising playback and precise correlation of ultrasonic vocalisations emissions with social interactions.
- Apply this method to analysis of mice containing the ASD-associated mutations.
This research will provide a critical link in our understanding of how mice use USVs to communicate and has relevance to numerous studies using mouse models for neuropsychiatric and communicative disorders. We predict that mice with ASD-associated mutations will show altered patterns of communication and insight from mechanistic studies in these mice will lead to a greater understanding of the biological underpinnings of communication impairments in ASD.
Leader: David Grayden
Collaborators: Emma Burrows (Florey Institute of Neuroscience and Mental Health) Neil McLachlan (Psychological Sciences) Anthony Hannan (Florey Institute of Neuroscience and Mental Health)
Sponsors: Melbourne Neuroscience Institute
Neuroengineering Research Laboratory
Convergence of engineering and IT with the life sciences
autism; computational neuroscience; mouse model; neuroengineering; speech recognition