Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning

Version of Record: June 20, 2017
Version of Record: November 30, 2016
Version of Record: September 29, 2015
Accepted Manuscript: August 26, 2015

Download
Cite
Share
CommentOpen annotations (there are currently 0 annotations on this page).

Altmetric provides a collated score for online attention across various platforms and media.
See more details

Abstract
Article and author information
Metrics

Abstract

Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.

Article and author information

Author details

Rui Ponte Costa

Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

For correspondence
rui.costa@cncb.ox.ac.uk

Competing interests
The authors declare that no competing interests exist.
Robert C Froemke

Skirball Institute for Biomolecular Medicine, Departments of Otolaryngology, Neuroscience and Physiology, New York University School of Medicine, New York, United States

Competing interests
The authors declare that no competing interests exist.
Per Jesper Sjöström

The Research Institute of the McGill University Health Centre, Department of Neurology and Neurosurgery, McGill University, Montreal, Canada

Competing interests
The authors declare that no competing interests exist.
Mark C W van Rossum

Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

Competing interests
The authors declare that no competing interests exist.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.