Noise-modulated multistable synapses in a Wilson-Cowan-based model of plasticity

Caroline A. Lea-Carnall, Lisabel I. Tanner, Marcelo A. Montemurro

Research output: Contribution to journalArticlepeer-review


Frequency-dependent plasticity refers to changes in synaptic strength in response to different stimulation frequencies. Resonance is a factor known to be of importance in such frequency dependence, however, the role of neural noise in the process remains elusive. Considering the brain is an inherently noisy system, understanding its effects may prove beneficial in shaping therapeutic interventions based on non-invasive brain stimulation protocols. The Wilson-Cowan (WC) model is a well-established model to describe the average dynamics of neural populations and has been shown to exhibit bistability in the presence of noise. However, the important question of how the different stable regimes in the WC model can affect synaptic plasticity when cortical populations interact has not yet been addressed. Therefore, we investigated plasticity dynamics in a WC-based model of interacting neural populations coupled with activity-dependent synapses in which a periodic stimulation was applied in the presence of noise of controlled intensity. The results indicate that for a narrow range of the noise variance, synaptic strength can be optimized. In particular, there is a regime of noise intensity for which synaptic strength presents a triple-stable state. Regulating noise intensity affects the probability that the system chooses one of the stable states, thereby controlling plasticity. These results suggest that noise is a highly influential factor in determining the outcome of plasticity induced by stimulation.
Original languageEnglish
JournalFrontiers in Computational Neuroscience
Publication statusPublished - 2 Feb 2023


  • Wilson-Cowan model
  • functional connectivity
  • homeostatic
  • multistability
  • neural mass model
  • plasticity
  • synapses


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