Imagine that the brain is like a party where different neural populations are interacting and communicating with each other. In order for these populations to change and adapt, their connections need to be flexible. That’s where synaptic plasticity comes in. It refers to the ability of synapses, the junctions between neurons, to strengthen or weaken based on the activity of the neurons. But how does this process get influenced by something like noise? Well, scientists have been exploring this question using a mathematical model called the Wilson-Cowan model, which simulates the dynamics of neural populations. They discovered that when noise is introduced into the model, it can actually optimize the strength of synapses within a certain range of noise intensity. It’s like adding just the right amount of spice to a dish to make it taste perfect! This finding has important implications for non-invasive brain stimulation therapies because it suggests that controlling the level of noise could potentially enhance the effectiveness of these therapies. So next time you find yourself at a noisy party, remember that noise isn’t always a bad thing. It might actually be playing a crucial role in shaping your brain!

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.

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