A single functional model of drivers and modulators in cortex
A distinction is commonly made between synaptic connections capable of evoking a response (“drivers”) and those that can alter ongoing activity but not initiate it (“modulators”). In this talk I'll propose that, in cortex, both drivers and modulators are an emergent property of the perceptual inference performed by cortical circuits. Hence, that there is a single underlying computational explanation for both forms of synaptic connection. This idea is illustrated using a predictive coding model of cortical perceptual inference. In this model all synaptic inputs are treated identically. However, functionally, certain synaptic inputs drive neural responses while others have a modulatory influence. I'll show that this model can account for driving and modulatory influences in bottom-up, lateral, and top-down pathways, and be used to simulate a wide range of neurophysiological phenomena including surround suppression, contour integration, gain modulation, and attention. The proposed computational model thus provides a single functional explanation for drivers and modulators and a single unified account of a diverse range of neurophysiological data.