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ABSTRACT: To efficiently drive many behaviors, sensory systems have to integrate the activity of large neuronal populations within a limited time window. These populations need to rapidly achieve a robust representation of the input image, probably through canonical computations such as divisive normalization. However, little is known about the dynamics of the corticocortical interactions implementing these rapid and robust computations. Here, we measured the real-time activity of a large neuronal population in V1 using voltage-sensitive dye imaging in behaving monkeys. We found that contrast gain of the population increases over time with a time constant of ∼30 ms and propagates laterally over the cortical surface. This dynamic is well accounted for by a divisive normalization achieved through a recurrent network that transiently increases in size after response onset with a slow swelling speed of 0.007-0.014 m/s, suggesting a polysynaptic intracortical origin. In the presence of a surround, this normalization pool is gradually balanced by lateral inputs propagating from distant cortical locations. This results in a centripetal propagation of surround suppression at a speed of 0.1-0.3 m/s, congruent with horizontal intracortical axons speed. We propose that a simple generalized normalization scheme can account for both the dynamical contrast response function through recurrent polysynaptic intracortical loops and for the surround suppression through long-range monosynaptic horizontal spread. Our results demonstrate that V1 achieves a rapid and robust context-dependent input normalization through a timely push-pull between local and lateral networks. We suggest that divisive normalization, a fundamental canonical computation, should be considered as a dynamic process.
Journal of Neuroscience 09/2012; 32(36):12558-69. · 7.11 Impact Factor
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ABSTRACT: Voltage sensitive dye imaging (VSDI) is the only technique that allows to directly measure neuronal activity over a large cortical population. It thus gives access to the dynamics of lateral interactions within or between cortical areas. However, VSDI signal suffers from a weak signal-to-noise ratio and processing methods are either rudimentary or dedicated to spatial or temporal denoising alone. Here we present an innovative method inspired by fMRI data processing, where the goal is to allow, for the first time, denoising of spatio-temporally inseparable VSDI signals and in the most challenging experimental condition, i.e. single trials in awake behaving monkeys. The method is based on a linear model (LM) decomposition of individual VSDI trials. The LM was designed meticulously by identifying all noise and signal components that are known to affect VSDI. We then compared its output against the classical methods based on blank division and detrending. LM proved to be significantly much more efficient to denoise spatial maps and temporal dynamics compared to these usual techniques. It also largely reduced trial-to-trial variability. These performances resulted in a four-fold improvement of signal-to-noise ratio and a two-fold increase of response detectability. Hence, with this method, fewer trials were needed to reach a high signal-to-noise ratio. Lastly, we showed that the LM method can accommodate for a large range of response dynamics, a crucial property for estimating spatial spread of activity or contrast dynamics. We believe that this method will make a strong contribution to imaging dynamics of population responses with high spatial and temporal resolution in trial-based experiments of awake animals.
NeuroImage 01/2011; 54(2):1196-210. · 5.89 Impact Factor
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Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14-17, 2008; 01/2008
