The Koha Code: A Biological Theory of Memory

TL;DR

The paper introduces the Koha model, a biological neural network theory explaining how neurons process, store information, and learn to detect patterns through dendritic spine computations and temporal coding.

Contribution

It presents a novel theory with a detailed process for signal filtration and a competitive learning algorithm for pattern detection in neurons.

Findings

Evidence for dendritic spine-based signal filtration

A new temporal coding mechanism within dendritic spines

A competitive learning algorithm for pattern detection

Abstract

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.