Deyue Kong*1, Lorenzo Butti1,Joe Barreto2 Greg Bond2, Matthias Kaschube1, Benjamin Scholl2
1Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany 2University of Colorado Anschutz Medical Campus, Department of Physiology and Biophysics, Aurora, Colorado, USA
*Email: kong@fias.uni-frankfurt.de
Introduction
Cortical computations arise through neuronal interactions and their dynamic reconfiguration in response to changing sensory contexts. Cortical interactions are proposed to engage distinct operational regimes that either amplify or suppress particular neuronal networks. A recent study in mouse primary visual cortex (V1) found competitive, suppressive interactions between nearby, similarly-tuned neurons, with exception of highly-correlated neuronal pairs showing facilitation [1]. It remains unclear whether such feature competition generalizes to cortical circuits with topographic organization, where neighboring neurons within columns exhibit similar tuning to features of visual stimuli, and distal excitatory axons preferentially target similarly-tuned columns.
Methods We investigated interactions between excitatory neurons in the ferret V1 and how network interactions depend on stimulus strength (contrast). We recorded the responses of layer 2/3 neurons to drifting gratings of eight directions at two contrast levels using 2-photon calcium imaging, while activating individual excitatory neurons with precise 2-photon optogenetics. We modeled and quantified the effect of target photostimulation on neural activity (inferred spike rate) during visual stimulation using a Poisson generalized linear model (GLM). We then used our model to estimate a target's influence on the surrounding neurons' activity and their stimulus coding properties. Results Our analyses revealed interactions that depended on cortical distance, stimulus properties, and functional similarity between neuron pairs. Influence of photostimulated neurons strongly depended on cortical distance, but overall exhibited net suppression. Suppression was weakest between nearby neurons (
