Synchronization is directly linked to oscillator settings and their interactions, being a behavior found in chemical, physical, and biological oscillators. Therefore, it is a quantitative method to verify the evolution of the collective behavior of coupled networks.
We study synchronization, criticality, and information theory in the field of computational neuroscience in a map-based neuron model, the KTH, a computationally efficient model capable of presenting a large number of neuronal behaviors, through its dynamics.
Our model evidences the synchronization seen in phase diagrams, analyzing the variables for the study of membrane potential, which is the means of communication between a neuron and another.
The bursts presented simultaneously begin and end their activities are characteristics of the synchrony condition. Furthermore, the model also shows chaotic bursting. The chaotic character must be considered in neurons as it has already been demonstrated in experiments with mollusks and crustaceans.
When the neuron receives an external perturbation, this directly interferes with the synchronization, according to the intensity of this input, the neurons can change their state.
The bursts presented simultaneously begin and end their activities are characteristics of the synchrony condition. Today, we know the role of synchronization in diseases such as pathological tremors, epilepsy, and others. Based on the facts mentioned above, we intend to study the regimes and architectures found by proposing viable tools for this type of analysis.