The local field potential (LFP) is an important brain signal to study due to its role in neural mechanisms such as visual perception and decision making, and in brain-computer interface development. Investigating LFPs often involves complex, biophysically detailed computational models, which face significant computational demands.Addressing this, we developed a simplified computational model of a cortical column representing the mouse V1 area, balancing morphological realism and practicality for large-network simulations. The model includes 17 sub-populations encompassing pyramidal neurons, PV, SST and VIP interneurons, using dendritic data from the Allen Brain Atlas for authentic replication of spontaneous firing rates.A novel feature of our model is a tool for designing customizable LFP recording probes, allowing for adjustable channels, angles, and depths. We validated the model by simulating a visual stimulation experiment while recording with a virtual 64-channel NeuroNexus Polytrode and compared our results with in-vivo mouse data. The resultant Current Source Density (CSD) profile exhibited the expected combination of sink and source rising upon visual stimulation, confirming the model's ability to capture LFP dynamics. We further showcased the model's versatility with two experiments: optogenetic inhibition of PV interneurons and partial AMPA receptor blockade. These experiments highlight the model's ability to simulate diverse conditions, including specific neuronal sub-population manipulation and receptor activity modulation.Our preliminary results indicate the model's efficacy in predicting extracellular potentials with reduced computational demands. This balance of simplicity and functionality makes it a valuable tool for neuroscience research, aiding in hypothesis testing and experimental design through in-silico methods.