Review Article | Published:

Control of synaptic plasticity in deep cortical networks

Nature Reviews Neuroscience volume 19, pages 166180 (2018) | Download Citation

Abstract

Humans and many other animals have an enormous capacity to learn about sensory stimuli and to master new skills. However, many of the mechanisms that enable us to learn remain to be understood. One of the greatest challenges of systems neuroscience is to explain how synaptic connections change to support maximally adaptive behaviour. Here, we provide an overview of factors that determine the change in the strength of synapses, with a focus on synaptic plasticity in sensory cortices. We review the influence of neuromodulators and feedback connections in synaptic plasticity and suggest a specific framework in which these factors can interact to improve the functioning of the entire network.

Key points

  • In addition to presynaptic and postsynaptic mechanisms, synaptic plasticity depends on neuromodulatory substances and feedback connections from higher-order cortical and thalamic brain regions

  • Synaptic plasticity in the brain depends on reward-prediction errors and on selective attention. Neuromodulatory systems code for the reward-prediction errors, and feedback connections from the response-selection stage mediate top-down attention effects

  • The combined influence of feedback connections and neuromodulatory substances on plasticity enables powerful learning rules for the training of 'deep', multilayered neuronal networks

  • Feedback connections project to cortical layers that are distinct from feedforward input, where they impinge on distal dendritic segments, separate excitatory neuronal populations or inhibitory interneurons

  • Feedback connections gate plasticity in cortical pyramidal neurons by promoting NMDA-receptor-driven calcium entry into dendrites and by disinhibiting the cortical column through activation of vasoactive-intestinal-peptide-positive interneurons (among others)

  • Synaptic tags are biochemical processes that make synapses eligible for plasticity. Neuromodulators released later can interact with tagged synapses to increase or decrease synaptic strength

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Acknowledgements

The authors thank S. Bohte, C. Pennartz, M. Sherman, V. Kehayas and H. Kennedy for helpful input and comments. The work was supported by the Netherlands Organisation for Scientific Research (NWO; ALW grant 823-02-010 to P.R.R.), the European Union Seventh Framework Programme (grant agreement 7202070 'Human Brain Project' to P.R.R. and European Research Council (ERC) grant agreement 339490 'Cortic_al_gorithms' to P.R.R.), the Swiss National Science Foundation (SNF; research grants 31003A-153448 and CRSII3-154453 to A.H. and the National Centre of Competence in Research (NCCR) SYNAPSY grant 51NF40-158776 to A.H.) and the International Foundation for Research in Paraplegia (to A.H.).

Author information

Affiliations

  1. Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands.

    • Pieter R. Roelfsema
  2. Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands.

    • Pieter R. Roelfsema
  3. Psychiatry Department, Academic Medical Center, Amsterdam, Netherlands.

    • Pieter R. Roelfsema
  4. Department of Basic Neurosciences, Geneva Neuroscience Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland.

    • Anthony Holtmaat

Authors

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Contributions

P.R.R. and A.H. researched data for the article, made substantial contributions to discussions of the content, wrote the article and reviewed and/or edited the manuscript before submission.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Pieter R. Roelfsema.

Glossary

Reward-prediction errors

(RPEs). Differences between the amount of reward that was expected and the amount that was obtained.

Reinforcement learning

Trial-and-error learning when interacting with an environment and experiencing rewards and punishments as consequences of the chosen actions.

Eligibility traces

Local parameters at the synapses of a network that determine whether they undergo plasticity upon reward-prediction errors during reinforcement learning.

Synaptic tags

Biochemical signals at synapses that determine whether they will undergo plasticity.

Error-backpropagation rule

A mathematical method used to calculate the contribution of connections to the error of a network with multiple layers between input and output.

Derivatives

The derivative of the error function to a synaptic weight is the rate of change of the error when changing the strength of a particular synapse.

Gradient descent

A mathematical optimization method that determines the direction of the vector of changes in all synaptic weights that causes the largest decrease in the error of the network.

Translation invariant

A property of an image processing system whereby the recognition of the object is independent of the object's location relative to the viewer.

Feedback alignment

A process in which, if the feedforward and feedback weights of a neural network are not reciprocal, error backpropagation causes feedforward weights to align; that is, to become more symmetrical.

Optokinetic reflex

The innate reflexive smooth eye movements elicited by large moving visual stimuli.

Frontal eye fields

Area of the frontal cortex involved in the planning of eye movements.

Martinotti cells

Somatostatin-expressing inhibitory interneurons with a characteristic morphology that target the dendritic tufts of pyramidal cells in various cortical layers.

Unsupervised learning

A type of learning in which the structure of unlabelled data is inferred as information about desired categorization is not provided.

Spike-timing-dependent plasticity

(STDP). A plasticity rule whereby the change in the strength of synapses depends on the relative timing of presynaptic and postsynaptic action potentials.

About this article

Publication history

Published

DOI

https://doi.org/10.1038/nrn.2018.6

Further reading