Title
Functional Connectivity And Tuning Curves In Populations Of Simultaneously Recorded Neurons
Abstract
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. Citation: Stevenson IH, London BM, Oby ER, Sachs NA, Reimer J, et al. (2012) Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons. PLoS Comput Biol 8(11): e1002775. doi:10.1371/journal.pcbi.1002775
Year
DOI
Venue
2012
10.1371/journal.pcbi.1002775
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
action potentials,electrophysiology,computer simulation,electrodes,computational biology
Biological system,Computer science,External variable,Artificial intelligence,Electrophysiology,Nerve net,Neural coding,Statistical model,Decoding methods,Genetics,Systems neuroscience,Machine learning,Encoding (memory)
Journal
Volume
Issue
ISSN
8
11
1553-734X
Citations 
PageRank 
References 
10
0.87
11
Authors
14