In what seems to be a subtle but significant step forward, neuroscientists have been able to study neural activity in the brain, of multitudes of neurons simultaneously, according to a new review paper published in Nature.
John P. Cunningham, assistant professor of statistics at Columbia University and Byron M. Yu who is an assistant professor of electrical and computer engineering and biomedical engineering at CMU, discuss the need to study and interpret neural activity concurrently utilising ‘dimensionality reduction’.
“One of the central tenets of neuroscience is that large numbers of neurons work together to give rise to brain function. However, most standard analytical methods are appropriate for analyzing only one or two neurons at a time,” notes Yu. “To understand how large numbers of neurons interact, advanced statistical methods, such as dimensionality reduction, are needed to interpret these large-scale neural recordings.”
It is through the functioning of clusters of neurons that most sensory, cognitive and motor interactions are carried out. According to Yu and Cunningham, recent studies have adopted dimensionality reduction to analyze these clusters and to find features that are not apparent at the level of individual neurons.
As ScienceDaily describes, “the idea behind dimensionality reduction is to summarize the activity of a large number of neurons using a smaller number of latent (or hidden) variables.”
“Dimensionality reduction methods are particularly useful to uncover inner workings of the brain, such as when we ruminate or solve a mental math problem, where all the action is going on inside the brain and not in the outside world. These latent variables can be used to trace out the path of ones thoughts.”
Yu and Cunningham believe that although in its infancy — at least compared to other analytic methods — dimensionality reduction might eventually lead to better understanding of how the brain functions, which in turn could make treatments of various conditions possible.
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(Image Credit: Neil Conway)