To understand how information is processed in neocortical circuits, it is crucial to map out in detail the role played by the individual neurons in the functioning of these circuits. This involves understanding how synaptic responses are integrated within the dendritic trees, and converted into trains of action potentials transmitted over the axon. In this thesis, using both simple and detailed compartmental models, I have studied the integrative behavior of the principal cell type of the cerebral cortex, the pyramidal neuron (PN). The objective has been to determine the rules of ?arithmetic? that PNs use to integrate the thousands of excitatory-inhibitory (E-I) influences impinging on their dendritic trees. Initially, I considered a specific computations that neurons must engage in, in order to effectively process the natural signals to which they are exposed. Recordings from cortical neurons indicate that their receptive fields are commonly subjected to multiplicative and divisive scaling operations. However, the synaptic, neuronal, and network-level mechanisms that underlie response scaling remain poorly understood. We used a detailed compartmental model of a neocortical pyramidal cell to study the ?scaling competence? of NMDA channels, which seem ideally suited to contribute to response scaling, in both the subthreshold and suprathreshold (i.e. somatic spiking) regimes. We found that NMDA-dependent scaling could be both accurate and precise over a very limited range of output as well as scaling factors. However, based on a recent study showing location-dependent modulatory effects in PN thin dendrites (Behabadi et al., 2007), we found a novel configuration of excitatory and inhibitory inputs on thin NMDA-rich dendrites that leads to accurate and precise scaling over a wide range of output firing rates and scaling factors.