AMI: An Auditory Machine Intelligence Algorithm for Predicting Sensory-Like Data

Authors : Emmanuel Ndidi OSEGİ, Vincent ANİREH

Pages : 71-89

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Publication Date : 2020-12-01

Article Type : Research Paper

Abstract :In this paper, we present the results of our experiments using a new biologically constrained machine intelligence algorithm based on neural processing in the auditory cortex called auditory machine intelligence insert ignore into journalissuearticles values(AMI);. This algorithm is an online learning technique for predicting sensory time series data i.e. data that comes in streams or a sequential order. The AMI algorithm is particularly inspired by the mismatch negativity effect which provides important evidence that the brain learns a statistical structure of the world it senses. We show through a number of experiments with popular benchmarks, how this algorithm may be applied in a real world sense. The results of these experiments have also been compared with two very popular techniques that have been used for time series predictions and are very encouraging.
Keywords : Auditory processing, biological machine intelligence, predictions, sensory data, time series