A neural mechanism for background information-gated learning based on axonal-dendritic overlaps

Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobi...

Full description

Saved in:
Bibliographic Details
Published in:PLoS computational biology 2015-03, Vol.11 (3), p.e1004155-e1004155
Main Authors: Mainetti, Matteo, Ascoli, Giorgio A
Format: Article
Language:English
Subjects:
Animals
Axons
Axons - physiology
Coleoptera - physiology
Computational Biology - methods
Dendrites
Dendrites - physiology
Knowledge representation
Learning - physiology
Memory
Models, Neurological
Nerve Net
Neural circuitry
Neural networks
Neurobiology
Neurons
Physiological aspects
Wasps - physiology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses. On the short time scale, this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron. We surmise that such "axonal-dendritic overlap" (ADO) constitutes the neural correlate of background information-gated (BIG) learning. The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts. The topographic organization of the mammalian cortex ensures that nearby neurons encode related information. Using neural network simulations, we demonstrate that ADO is a suitable mechanism for BIG learning. We model knowledge as associations between terms, concepts or indivisible units of thought via directed graphs. The simplest instantiation encodes each concept by single neurons. Results are then generalized to cell assemblies. The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1004155