Emergence of Neuronal Synchronisation in Coupled Areas

Abstract

One of the most fundamental questions in the field of neuroscience is the emergence of synchronous behaviour in the brain, such as phase, anti-phase, and shift-phase synchronisation. In this work, we investigate how the connectivity between brain areas can influence the phase angle and the neuronal synchronisation. To do this, we consider brain areas connected by means of excitatory and inhibitory synapses, in which the neuron dynamics is given by the adaptive exponential integrate-and-fire model. Our simulations suggest that excitatory and inhibitory connections from one area to another play a crucial role in the emergence of these types of synchronisation. Thus, in the case of unidirectional interaction, we observe that the phase angles of the neurons in the receiver area depend on the excitatory and inhibitory synapses which arrive from the sender area. Moreover, when the neurons in the sender area are synchronised, the phase angle variability of the receiver area can be reduced for some conductance values between the areas. For bidirectional interactions, we find that phase and anti-phase synchronisation can emerge due to excitatory and inhibitory connections. We also verify, for a strong inhibitory-to-excitatory interaction, the existence of silent neuronal activities, namely a large number of excitatory neurons that remain in silence for a long time.

Enactive-Dynamic Social Cognition and Active Inference

“…The synchronisation occurs as complex behaviour emerging from the interaction between relatively simple systems (Rihani, 2002;Wolfram, 2018), known as stochastic resonance (Gammaitoni et al, 1998;Lucarini, 2019). Other examples include synchronised neurons, individually interacting as if they knew their peer's behaviour when they could not possibly (Lewis et al, 2004;Friston and Frith, 2015;Balconi et al, 2020;Protachevicz et al, 2021). It is worth noting that, while pendulums do not predict each other's states, they also do not know each other's states.…”

In-phase and anti-phase bursting dynamics and synchronisation scenario in neural network by varying coupling phase

“…But, later it was proved wrong [3] and a stable anti-phase oscillations in electrically coupled relaxation oscillators were reported [15]. The stochastic transitions between in-phase and anti-phase synchronisations in neural oscillators are still of great interest even after these many years [16,17]. Researchers have proposed different modifications in inhibitory and excitatory connections to induce anti-and in-phase oscillations [8,18].…”

The effect of time delay for synchronisation suppression in neuronal networks

“…In such a model it was reported that the generation of the action potential in the cell membrane is linked to variations in the ionic currents of potassium, sodium and a current defined by them as leak. Although varous mathematical models have been proposed to reproduce the neuronal behaviour [14,15], the HH neuron is still one of the most actual approaches to neuronal dynamics, inspiring several studies in the field of neuroscience [16,17].…”