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Friday, April 20, 2018

Machine-learning system processes sounds like humans do | MIT News

"Neuroscientists train a deep neural network to analyze speech and music" says Anne Trafton, MIT News Office.

MIT neuroscientists have developed a machine-learning system that can process speech and music the same way that humans do.
Photo: Chelsea Turner/MIT
Using a machine-learning system known as a deep neural network, MIT researchers have created the first model that can replicate human performance on auditory tasks such as identifying a musical genre.

This model, which consists of many layers of information-processing units that can be trained on huge volumes of data to perform specific tasks, was used by the researchers to shed light on how the human brain may be performing the same tasks.

“What these models give us, for the first time, is machine systems that can perform sensory tasks that matter to humans and that do so at human levels,” says Josh McDermott, the Frederick A. and Carole J. Middleton Assistant Professor of Neuroscience in the Department of Brain and Cognitive Sciences at MIT and the senior author of the study. “Historically, this type of sensory processing has been difficult to understand, in part because we haven’t really had a very clear theoretical foundation and a good way to develop models of what might be going on.”

The study, which appears in the April 19 issue of Neuron, also offers evidence that the human auditory cortex is arranged in a hierarchical organization, much like the visual cortex. In this type of arrangement, sensory information passes through successive stages of processing, with basic information processed earlier and more advanced features such as word meaning extracted in later stages.

MIT graduate student Alexander Kell and Stanford University Assistant Professor Daniel Yamins are the paper’s lead authors. Other authors are former MIT visiting student Erica Shook and former MIT postdoc Sam Norman-Haignere. 

Modeling the brain
When deep neural networks were first developed in the 1980s, neuroscientists hoped that such systems could be used to model the human brain. However, computers from that era were not powerful enough to build models large enough to perform real-world tasks such as object recognition or speech recognition.

Over the past five years, advances in computing power and neural network technology have made it possible to use neural networks to perform difficult real-world tasks, and they have become the standard approach in many engineering applications. In parallel, some neuroscientists have revisited the possibility that these systems might be used to model the human brain.
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Journal Reference:
Journal Alexander J.E. Kell, Daniel L.K. Yamins, Erica N. Shook, Sam V. Norman-Haignere, Josh H. McDermott. A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy. Neuron, 2018; DOI: 10.1016/j.neuron.2018.03.044

Source: MIT News

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