Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction. (April 2019)

• Record Type: Journal Article
• Authors: Williamson, Ryan C.; Doiron, Brent; Smith, Matthew A.; Yu, Byron M.
• Is Part Of: Current opinion in neurobiology. Volume 55(2019)
• Page Start: 40
• Page End: 47
• Publication Date: 2019-04
• Subjects: Neurobiology -- Periodicals; 573.8
• DOI: 10.1016/j.conb.2018.12.009
• Languages: English
• ISSNs: 0959-4388

Abstract

Highlights: The interplay of neuronal recordings and network models is becoming ever stronger. Population activity structure provides common ground for incisive comparisons. Dimensionality reduction can be used to identify population activity structure. This approach is used to study working memory, decision making, motor control, etc. Abstract : A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.