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.
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.MIT neuroscientists have developed a machine-learning system that can process speech and music the same way that humans do.
Photo: Chelsea Turner/MIT
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.
Read more...
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