Going beyond standard backpropagation and building on burst-dependent synaptic plasticity (BDSP) (A Payeur, Nature Neuroscience, 2021), here we have integrated our context-sensitive two-point neuron (CS-TPN) model into biologically plausible two-point neuron (TPN)-driven BDSP. In our model, CS-TPN is divided in to two integration zones: Somatic Integration Zone (SIZ) and Apical Integration Zone (AIZ). At the AIZ, different contextual inputs, including but not limited to universal context (Cu), distal context (Cd), proximal context (Cp), and credit assignment (Ce) in terms of contextual voltages evolve based on their independent differential equations. The integrated context (C) is calculated using the evolved contextual voltages that eventually effect the SIZ by modulating the receptive current: amplification if C is high, suppression if C is low. In this scheme, information is encoded in neurons firing (single spike=blue, burst of spikes=red). CS-TPN evidently differentiates between irrelevant, relevant, and very relevant information i.e., no spike, single spike, and burst of spikes respectively, which improves learning. In the raster plots, it can be seen that CS-TPNs remain largely silent when information is less relevant and vocal (bursting) otherwise. It can also be observed that TPNs are firing more often than CS-TPNs.