Computational modeling reveals that individual neurons can independently separate overlapping information streams using calcium-mediated action potentials within their dendritic branches, challenging the prevailing view that such complex processing requires entire neural networks. The research demonstrates how dendritic calcium channels enable single cells to parse multiple simultaneous inputs without cross-contamination. This finding fundamentally reframes our understanding of neural computation, suggesting the basic unit of brain processing may be far more sophisticated than previously recognized. Individual neurons appear capable of the kind of signal separation typically attributed to multi-neuron circuits, potentially explaining how the brain achieves remarkable computational efficiency with relatively modest energy expenditure. The discovery has profound implications for understanding cognitive disorders where signal processing breaks down, such as schizophrenia and ADHD, where attention filtering fails. It also suggests new approaches for treating neurodegenerative diseases by targeting dendritic calcium dynamics rather than just synaptic transmission. However, the work relies on computational models rather than direct biological validation, and translating these insights to therapeutic interventions remains speculative. This represents a potentially paradigm-shifting advance in neuroscience that could reshape how we approach brain-based therapies.