Feature selectivity of cortical neurons, one example of functional properties in the brain, is the ability of neurons to respond to particular stimulus attributes - e.g. the receptive field of a neuron in the primary visual cortex (V1) with respect to object movement direction. This thesis contributes to understanding how feature selectivity arises in mouse V1. It is divided into two parts, each based on distinct approaches to elucidate visual processing mechanisms, the first at a population level and the second at the single neuron level. First, on a population level, I have developed tools towards an eventual project that combines 2-photon optogenetics, 2-photon imaging and traditional whole-cell electrophysiology to map functional connectivity in V1. This map will provide a link between cell tuning (i.e. cell function) and network architecture, enabling quantitative and qualitative distinction between two extreme scenarios in which cells in mouse V1 are either randomly connected, or are associated in specialized subnetworks. Here I describe the technical validation of the method, with the main focus on finding the appropriate biological preparation and reagents. Second, based on whole-cell patch recordings of single mouse V1 neurons in vivo, I characterize the neuronal input-output (I/O) transfer function using current and conductance inputs, the latter intended to mimic the biophysical properties of synapses in a functional context. I employ a novel closed-loop in vivo protocol based on a combination of current, voltage and dynamic clamp recording modes. I first measure the basic I/O transfer function of a given neuron with current and conductance steps, under current and dynamic clamp, respectively. I then measure the visually evoked spiking output, under current clamp, and the synaptic conductance input, under voltage clamp, to that neuron. Finally, I reintroduce variations of the visually-evoked conductance input to the same cell under dynamic clamp. In that manner, I describe an I/O transfer function which allows a characterization of the mathematical operations performed by the neuron during functional processing. Furthermore, modifications of the relative scaling and the temporal characteristics of the excitatory and inhibitory components of the reintroduced synaptic input, enables dissection of each component's role in shaping the spiking output, as well as to infer overall differences between various physiological cell types (e.g. regular-adapting, presumably excitatory, versus fast-spiking, presumably inhibitory, neurons). Finally, examination of the transfer functions, in particular their dependence on temporal modifications, provides insights on the relationship between the neuronal code and the biophysical properties of neurons and their network.