A Dynamic Neural Field Model of Mesoscopic Cortical Activity Captured with Voltage-Sensitive Dye Imaging

A neural field model is presented that captures the essential non-linear characteristics of activity dynamics across several millimeters of visual cortex in response to local flashed and moving stimuli. We account for physiological data obtained by voltage-sensitive dye (VSD) imaging which reports mesoscopic population activity at high spatio-temporal resolution. Stimulation included a single flashed square, a single flashed bar, the line-motion paradigm – for which psychophysical studies showed that flashing a square briefly before a bar produces sensation of illusory motion within the bar – and moving squares controls. We consider a two-layer neural field (NF) model describing an excitatory and an inhibitory layer of neurons as a coupled system of non-linear integro-differential equations. Under the assumption that the aggregated activity of both layers is reflected by VSD imaging, our phenomenological model quantitatively accounts for the observed spatio-temporal activity patterns. Moreover, the model generalizes to novel similar stimuli as it matches activity evoked by moving squares of different speeds. Our results indicate that feedback from higher brain areas is not required to produce motion patterns in the case of the illusory line-motion paradigm. Physiological interpretation of the model suggests that a considerable fraction of the VSD signal may be due to inhibitory activity, supporting the notion that balanced intra-layer cortical interactions between inhibitory and excitatory populations play a major role in shaping dynamic stimulus representations in the early visual cortex.Author Summary: Understanding the functioning of the primary visual cortex requires characterization of the non-linear dynamics that underlie visual perception and of how the cortical architecture gives rise to these dynamics. Recent advances in real-time voltage-sensitive dye (VSD) imaging permit recording of cortical population activity with high spatial and temporal resolution. This wealth of data can be related to cortical function, dynamics, and architecture by computational modeling. Here we used a mesoscopic neural field model to describe brain dynamics at the population level as measured by VSD imaging. Introduced in 1972 by Wilson and Cowan, these models are derived from statistical mechanics to analyze the collective properties of large numbers of neurons. For simplicity, the cortical planar tissue is assumed to contain only two types of homogeneously distributed neurons (excitatory and inhibitory) that interact via recurrent lateral connections. This study shows 1) how a concise neural field model can simulate VSD data quantitatively in space and time by identifying the underlying non-linear dynamics, 2) how such a model can support hypotheses about visual information processing, and 3) how the model can be linked to the neuronal architecture.