Imagine a bustling dance floor where groups of dancers, both enthusiastic and reserved, intermingle and create a captivating performance. In a similar fashion, networks of excitatory and inhibitory neurons in the brain engage in an intricate choreography that gives rise to spontaneous activity. This study investigates the underlying mechanisms that allow these neuronal populations to organize themselves near a critical point known as the Bogdanov-Takens bifurcation. By developing a neural field model, researchers demonstrate how short-term synaptic depression and long-term synaptic plasticity play key roles in fine-tuning the network dynamics close to this pivotal point. Furthermore, their model aligns with the directed percolation model, shedding light on the nature of transitions between different states in neuronal populations. It’s like peering into a hidden world of dancing neurons and unraveling the secrets behind their synchronized moves. To uncover more about this mesmerizing phenomenon and its implications for understanding brain function, dive into the full research article!

Dynamics of an interconnected population of excitatory and inhibitory spiking neurons wandering around a Bogdanov-Takens (BT) bifurcation point can generate the observed scale-free avalanches at the population level and the highly variable spike patterns of individual neurons. These characteristics match experimental findings for spontaneous intrinsic activity in the brain. In this paper, we address the mechanisms causing the system to get and remain near this BT point. We propose an effective stochastic neural field model which captures the dynamics of the mean-field model. We show how the network tunes itself through local long-term synaptic plasticity by STDP and short-term synaptic depression to be close to this bifurcation point. The mesoscopic model that we derive matches the directed percolation model at the absorbing state phase transition.

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