The cortex must maintain balanced levels of neural activity to correctly integrate inputs and to provide contextually meaningful outputs. Neuronal excitation is counterbalanced by various forms of inhibition such as spike frequency adaptation, short- and long-term depression at excitatory synapses, and inhibitory cell coupling. When excitatory activity is low, homeostatic mechanisms enhance network excitability. When excitation is stronger than inhibition, network bursts of activity arise that also need to be dampened lest they run out of control. A disruption of the fine balance between excitatory and inhibitory systems is observed in an increasing numbers of brain disorders such as epilepsy, mental retardation and autism. This thesis aims to study aspects of the regulation of excitation in cortical networks at a molecular, cellular, network and disease level of investigation. We present in the first chapter an investigation of the impact of network bursts on standard spike-timing dependent plasticity (STDP) events in acute cortical brain slices. We discovered that these two kinds of events, which are the most well-established ways to induce synaptic plasticity in vitro, can block each other as well as cooperate to change the amplitude and direction of plasticity. Because the outcome of the interaction depends on the precise timing of network bursts relative to a STDP event in a single synaptic pathway, the observed network-timing dependent plasticity provides a novel mechanism for the modulation of local synaptic plasticity by the network level of activity, a form of global control of local synaptic plasticity. We hypothesized that network bursts consume extracellular resources needed to potentiate, restricting potentiation to active synapses at the burst onset while yielding depression of synapses activated at later times. As we demonstrate with simulation, this simple mechanism supports paradigm-shifting implications for models of learning, solving a persistent and fundamental problem since BCM (Bienenstock Cooper Munro, 1981) to incorporate synaptic learning into realistic neuronal networks while simultaneously establishing and maintaining excitatory-inhibitory balance. The proposed mechanism also links experimental evidence for interactions between STDP and network activity to observations of self-organized criticality in neural systems, a regime known to be optimal for information coding. In the second chapter, we investigated the effect of the loss of NLGN4 protein on synapse function and on network response to electrical stimulation. The neuroligin family of genes encodes for trans-synaptic proteins involved in synaptic differentiation and maturation. Former studies have reported that both NLGN1-KO and NLGN2-KO give rise to pathologic deregulations of the balance between excitation and inhibition in neural networks, and the presence of loss-of-function mutations in NLGN4 in a small number of autistic patients indicates a possible link between Neuroligin-4 gene and autism. Using extra-cellular stimulation and intra-cellular recordings in acute brain slices, we observed a decreased response of both excitatory and inhibitory response in NLGN4-KO animals, suggesting that NLGN4 is expressed at both excitatory and inhibitory synapses. Furthermore, we observed that the NLGN4-KO resulted in a significantly larger decrease of the excitatory response than of the inhibitory response, thus yielding a hypo-excitable network. The final chapter consists of the characterization of excitatory pathways in acute slices of the somato-sensory cortex. In the first study, we used chemical stimulation to promote the emergence of spontaneous recurrent activity and observed waves of activity originating from the cortical layer five. In the second study, we performed whole-cell patch-clamping of layer five pyramidal cells along with recording local field potentials (LFP). With the systematic layer-wise application of electrical stimulation at various frequencies coupled with LFP recordings, we obtained a frequency-dependent mapping of cortical inputs at all cortical layers at both the cellular and the network levels. Using this data, we developed a virtual framework for performing an analogous mapping in the virtual cortical column of the Blue Brain Project (BBP). This helped us understand the contribution of each cell population in the network response to stimulation. The framework also served as a validation test for the BBP cortical models.