Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons
A) Schematic illustrating how tuning properties may be related to functional connectivity. The tuning properties of observed neurons (black) are a direct result of input they receive from peripheral neurons (blue and red). Even when observed neurons do not have a direct relationship to external variables, each neuron may have apparent tuning caused by the input it receives from peripheral neurons and shaped by interactions with other observed neurons. B) A linear-nonlinear-Poisson model that includes both tuning properties as well as interactions or coupling between neurons. The firing rate of each neuron is modeled as a weighted sum of external variables as well as the activities of other observed neurons passed through an exponential nonlinearity. C) A toy example where tuning properties are explained away using coupling. In two simulated networks only neuron 1 is directly related to the external world (with Gaussian tuning). However, neurons 2 and 3 have tuning due to the input they receive from neuron 1 (middle). If these interactions are estimated, then coupling can fully explain the observed tuning (bottom).