Most models of neural computation used in artificial intelligence and neuroscience use point neurons, attributing computational complexity to the network level. This approach overlooks the computational complexity of single biological neurons. Neural dendrites exhibit many different nonlinear dynamics, temporal-spatial filtering, and internal feedback and amplification mechanisms (London & Häusser 2005). Abstracting the entire dendritic arbor into a single compartment thus omits useful computational properties (Poirazi et al. 2003). Three main factors contribute to the integration process: (1) dendritic morphology, (2) voltage-gated ion channels, and (3) non-linear activation of NMDA synapses. Understanding backpropagating action potentials (bAPs), NMDA-spikes, calcium-spikes, and their interactions can reveal the true computational power of single neurons. However, most dendritic phenomena have been studied in isolation, with limited research on their interactions. Models incorporating NMDA-spikes for sequence processing, non-linear processing, or logic often neglect the calcium integration zone (Leugering et al. 2019), their timescales (Poirazi et al. 2003), or other morphological features that influence the dendritic integration process (Quaresima et al. 2022). Conversely, models that focus on the functional coupling between the soma and a calcium enriched second integration zone in the apical dendrite, do not allow for NMDAr dynamics to influence neural processing(Yi et al. 2017). Jones and Körding (2021) have shown that modeling dendritic constraints in isolation worsens the neurons’ computational capabilities, while the synergy of several dendritic mechanisms that influence each other can even outperform classical ANNs. This work examines how dendritic mechanisms and their interactions contribute to single neuron computation, highlighting gaps in our current understanding. For instance, the influence of apical NMDA-spikes on the calcium integration zone is still unclear, and bAPs affect not only synaptic plasticity but also forward integration in the dendrite. Our perspective points towards a future where comprehensive and integrated models of active dendritic mechanisms offer a more accurate understanding of neural computation.