Prediction of Acoustic Residual Inhibition of Tinnitus using a Brain-Inspired Spiking Neural Network Model

Philip Sanders, Zohreh Doborjeh, Maryam Doborjeh, Nikola Kasabov, Grant Searchfield

Research output: Contribution to journalArticlepeer-review

33 Downloads (Pure)

Abstract

Auditory Residual Inhibition (ARI) is a temporary suppression of tinnitus that occurs in some people following the presentation of masking sounds. Differences in neural response to ARI stimuli may enable classification of tinnitus and a tailored approach to intervention in the future. In an exploratory study, we investigated the use of a brain-inspired artificial neural network to examine the effects of ARI on electroencephalographic function, as well as the predictive ability of the model. Ten tinnitus patients underwent two auditory stimulation conditions (Constant and Amplitude Modulated broadband noise) at two time points and were then characterised as responders or non-responders, based on whether they experienced ARI or not. Using a spiking neural network model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data, capturing the neural dynamic changes before and after stimulation. Results indicated that the model may be used to predict the effect of auditory stimulation on tinnitus on an individual basis. This approach may aid in the development of predictive models for treatment selection.
Original languageEnglish
Article number52
Pages (from-to)1-18
Number of pages18
JournalBrain Sciences
Volume11
Issue number1
DOIs
Publication statusPublished - 5 Jan 2021

Keywords

  • Residual Inhibition
  • Amplitude Modulated
  • Tinnitus
  • Spiking Neural Network
  • Prediction
  • Individualised treatment

Fingerprint Dive into the research topics of 'Prediction of Acoustic Residual Inhibition of Tinnitus using a Brain-Inspired Spiking Neural Network Model'. Together they form a unique fingerprint.

Cite this