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Role of experience and oscillations in transforming a rate code into a temporal code

Author

Listed:
  • M. R. Mehta

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • A. K. Lee

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • M. A. Wilson

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

Abstract

In the vast majority of brain areas, the firing rates of neurons, averaged over several hundred milliseconds to several seconds, can be strongly modulated by, and provide accurate information about, properties of their inputs. This is referred to as the rate code. However, the biophysical laws of synaptic plasticity require precise timing of spikes over short timescales (

Suggested Citation

  • M. R. Mehta & A. K. Lee & M. A. Wilson, 2002. "Role of experience and oscillations in transforming a rate code into a temporal code," Nature, Nature, vol. 417(6890), pages 741-746, June.
    • Handle: RePEc:nat:nature:v:417:y:2002:i:6890:d:10.1038_nature00807
    DOI: 10.1038/nature00807
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    Cited by:

    1. Matthieu Gilson & Tomoki Fukai, 2011. "Stability versus Neuronal Specialization for STDP: Long-Tail Weight Distributions Solve the Dilemma," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-18, October.
    2. Eric Reifenstein & Martin Stemmler & Andreas V M Herz & Richard Kempter & Susanne Schreiber, 2014. "Movement Dependence and Layer Specificity of Entorhinal Phase Precession in Two-Dimensional Environments," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    3. Louis Kang & Taro Toyoizumi, 2024. "Distinguishing examples while building concepts in hippocampal and artificial networks," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    4. Gosak, Marko & Markovič, Rene & Marhl, Marko, 2012. "The role of neural architecture and the speed of signal propagation in the process of synchronization of bursting neurons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2764-2770.
    5. Matthieu Gilson & Tomoki Fukai & Anthony N Burkitt, 2012. "Spectral Analysis of Input Spike Trains by Spike-Timing-Dependent Plasticity," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-22, July.
    6. Dhanya Parameshwaran & Upinder S Bhalla, 2013. "Theta Frequency Background Tunes Transmission but Not Summation of Spiking Responses," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    7. Sara Mahallati & James C Bezdek & Milos R Popovic & Taufik A Valiante, 2019. "Cluster tendency assessment in neuronal spike data," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-29, November.

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