How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.
Details
Title
Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons
Author
Stevenson, Ian H. : University of California, Berkeley London, Brian M. : Northwestern University Oby, Emily R. : Northwestern University Sachs, Nicholas A. : Northwestern University Reimer, Jacob : Baylor College of Medicine Englitz, Bernhard : Oregon Health and Science University David, Stephen V. : Oregon Health and Science University Shamma, Shihab A. : University of Maryland Blanche, Timothy J. : University of California, Berkeley Mizuseki, Kenji : Rutgers University Zandvakili, Amin : Albert Einstein College of Medicine Hatsopoulos, Nicholas G. : University of Chicago Miller, Lee E. : Northwestern University Kording, Konrad P. : Northwestern University
Funding Information
Chicago Community Trust and National Institutes of Health, 1R01NS063399 Chicago Community Trust and National Institutes of Health, 2P01NS044393 National Institutes of Health, R01NS048845 National Institutes of Health, R01NS053603 NCRR-NIH, Clinical and Translational Science Awards Program, UL1RR0254741 NIH-NINDS, R01NS45853 National Institutes of Health, R01DC005779 National Institutes of Health, K99DC010439 National Institutes of Health, 5R21NS066260 National Institutes of Health, EY016774 DFG, fellowship, EN919/1-1
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