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Formation and maintenance of neuronal assemblies through synaptic plasticity

Author

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  • Ashok Litwin-Kumar

    (Program for Neural Computation, Carnegie Mellon University and University of Pittsburgh
    University of Pittsburgh
    Center for the Neural Basis of Cognition)

  • Brent Doiron

    (University of Pittsburgh
    Center for the Neural Basis of Cognition)

Abstract

The architecture of cortex is flexible, permitting neuronal networks to store recent sensory experiences as specific synaptic connectivity patterns. However, it is unclear how these patterns are maintained in the face of the high spike time variability associated with cortex. Here we demonstrate, using a large-scale cortical network model, that realistic synaptic plasticity rules coupled with homeostatic mechanisms lead to the formation of neuronal assemblies that reflect previously experienced stimuli. Further, reverberation of past evoked states in spontaneous spiking activity stabilizes, rather than erases, this learned architecture. Spontaneous and evoked spiking activity contains a signature of learned assembly structures, leading to testable predictions about the effect of recent sensory experience on spike train statistics. Our work outlines requirements for synaptic plasticity rules capable of modifying spontaneous dynamics and shows that this modification is beneficial for stability of learned network architectures.

Suggested Citation

  • Ashok Litwin-Kumar & Brent Doiron, 2014. "Formation and maintenance of neuronal assemblies through synaptic plasticity," Nature Communications, Nature, vol. 5(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6319
    DOI: 10.1038/ncomms6319
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    Cited by:

    1. Gabriel Koch Ocker & Ashok Litwin-Kumar & Brent Doiron, 2015. "Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-40, August.
    2. Mizusaki, Beatriz E.P. & Agnes, Everton J. & Erichsen, Rubem & Brunnet, Leonardo G., 2017. "Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 279-286.

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