  This work introduces the Koha model, a new theory that aims to explain two
unresolved phenomena within biological neural networks: How information is
processed and stored within neural circuits, and how neurons learn to become
pattern detectors. In the Koha model, the dendritic spines of a neuron serve as
computational units that scan for precise spike patterns in their synaptic
inputs. The model proposes the existence of a temporal code within each
dendritic spine, which is used for the dampening or amplification of signals,
depending on the temporal information of incoming spike trains. Compelling
evidence is provided and a concrete process is described for how signal
filtration occurs within spine necks. A competitive learning algorithm is then
proposed that describes how neurons use their internal temporal codes to become
pattern detectors.
