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Primary visual cortex (V1) provides crucial insights into the selectivity andemergence of specific output features such as orientation tuning. Tuning andselectivity of cortical neurons in mouse visual cortex is not equivocallyresolved so far. While many in-vivo experimental studies found inhibitoryneurons of all subtypes to be broadly tuned for orientation other studiesreport inhibitory neurons that are as sharply tuned as excitatory neurons.These diverging findings about the selectivity of excitatory and inhibitorycortical neurons prompted us to ask the following questions: (1) How differentor similar is the cortical computation with that in previously describedspecies that relies on map? (2) What is the network mechanism underlying thesharpening of orientation selectivity in the mouse primary visual cortex? Here,we investigate the above questions in a computational framework with arecurrent network composed of Hodgkin-Huxley (HH) point neurons. Our corticalnetwork with random connectivity alone could not account for all theexperimental observations, which led us to hypothesize, (a) Orientationdependent connectivity (b) Feedforward afferent specificity to understandorientation selectivity of V1 neurons in mouse. Using population (orientationselectivity index) OSI as a measure of neuronal selectivity to stimulusorientation we test each hypothesis separately and in combination againstexperimental data. Based on our analysis of orientation selectivity (OS) datawe find a good fit of network parameters in a model based on afferentspecificity and connectivity that scales with feature similarity. We concludethat this particular model class best supports data sets of orientationselectivity of excitatory and inhibitory neurons in layer 2/3 of primary visualcortex of mouse.

Afferent specificity, feature specific connectivity influence orientation selectivity: A computational study in mouse primary visual cortex