Jan 10, 2013

Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex

PLoS Computational Biology
Pengsheng ZhengJochen Triesch

Abstract

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. At the same time, new synaptic connections are constantly being created and individual synaptic connection strengths show substantial fluctuations across time. It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. In the network, associative synaptic plasticity in the form of STDP induces a rich-get-richer dynamics among synapses, while homeostatic mechanisms induce competition. Under distinctly different initial conditions, the ensuing self-o...Continue Reading

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  • Citations30

References

Mentioned in this Paper

Fluctuation
Cortex Bone Disorders
Adrenal Cortex Diseases
Memory Training
Neurons
Brain
Neuronal Plasticity
Neural Network Simulation
Structure of Cortex of Kidney
Genus Hippocampus

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