Journal
FRONTIERS IN NEUROANATOMY
Volume 10, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnana.2016.00063
Keywords
synaptic plasticity; effective connectivity; transfer entropy; learning; potential synapse; memory consolidation; storage capacity; spacing effect
Categories
Funding
- INTEL
- Kavli Foundation
- National Science Foundation [0855272, 1219212, 1516527]
- Direct For Computer & Info Scie & Enginr [1219212] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [0855272] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems [1219212] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1516527] Funding Source: National Science Foundation
Ask authors/readers for more resources
Learning and memory is commonly attributed to the modification of synaptic strengths in neuronal networks. More recent experiments have also revealed a major role of structural plasticity including elimination and regeneration of synapses, growth and retraction of dendritic spines, and remodeling of axons and dendrites. Here we work out the idea that one likely function of structural plasticity is to increase effectual connectivity in order to improve the capacity of sparsely connected networks to store Hebbian cell assemblies that are supposed to represent memories. For this we define effectual connectivity as the fraction of synaptically linked neuron pairs within a cell assembly representing a memory. We show by theory and numerical simulation the close links between effectual connectivity and both information storage capacity of neural networks and effective connectivity as commonly employed in functional brain imaging and connectome analysis. Then, by applying our model to a recently proposed memory model, we can give improved estimates on the number of cell assemblies that can be stored in a cortical macrocolumn assuming realistic connectivity. Finally, we derive a simplified model of structural plasticity to enable large scale simulation of memory phenomena, and apply our model to link ongoing adult structural plasticity to recent behavioral data on the spacing effect of learning.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available