Channel noise effects on neural synchronization

Abstract

Synchronization in neural networks is strongly tied to the implementation of cognitive processes, but abnormal neuronal synchronization has been linked to a number of brain disorders such as epilepsy and schizophrenia. Here we examine the effects of channel noise on the synchronization of small Hodgkin-Huxley neuronal networks. The principal feature of a Hodgkin-Huxley neuron is the existence of protein channels that transition between open and closed states with voltage dependent rate constants. The Hodgkin-Huxley model assumes infinitely many channels, so fluctuations in the number of open channels do not affect the voltage. However, real neurons have finitely many channels which lead to fluctuations in the membrane voltage and modify the timing of the spikes, which may in turn lead to large changes in the degree of synchronization. We demonstrate that under mild conditions, neurons in the network reach a steady state synchronization level that depends only on the number of neurons in the network. The channel noise only affects the time it takes to reach the steady state synchronization level.

Synchronization of stochastic complex networks with time-delayed coupling

“…[28,29] Meanwhile, random factors were taken into account when analyzing network synchronization. [30][31][32][33][34][35][36][37] It was discovered that increasing noise intensity may be beneficial to synchronization when noise passes through all state variables. [26] Nevertheless, there is no systematic study on the influence of both random noise and time delay on network synchronization by using the MSF method.…”

A Computational Model of the Brain Cortex and Its Synchronization

“…These modern brain imaging techniques are valuable as they provide data for the approval of the computational models focusing on understanding the relationship between cognition and the brain [ 1 – 3 ]. The models developed for the cortex are ranging from a detailed single neuron model [ 4 ] to the mass models where the collective activity of a population of the neuron is considered [ 3 ].…”

“…The measurement of synchronization has many applications in monitoring the activity of the brain and the degree of interaction between different brain parts, which gives us a thorough idea about brain functionality both normal and deficiency, where synchronization can give us a right indication about the degree of the engagement of two or more neuronal groups in some mental task, and the more coherence between the firing patterns of these neuronal groups, and the physiological interactivity between them [ 4 ].…”