Axonal delay dispersion decides whether a neuron detects an event or a sequence, and predicts cortical column diameter

What the math gives to ML

The paper supplies a concrete temporal-computation primitive: synaptic delays form a physical matching key, and a branch fires only when delayed arrivals fall inside a narrow coincidence window and exceed a calcium-like threshold. The transferable asset is the algebraic condition that a delay difference compensates an input sequence interval, allowing the same module to distinguish unordered events from ordered sequences. A practical neural implementation is a bank of branches with fixed or learnable delays, soft coincidence pooling during training, and thresholded branch outputs before a recurrent, state-space, or attention-based temporal encoder. The main falsifiable prediction is that increasing delay dispersion should improve order selectivity while reducing performance on order-invariant event detection.