The provided code is a computational model designed to simulate the dynamics of two populations of neurons using a mean-field approach. Here’s a breakdown of its biological basis:
vm
): The variables vm1
and vm2
denote the membrane potentials of the respective populations, influenced by synaptic input and intrinsic parameters such as mu
(background current) and Iext1
, Iext2
(external currents).wm1
and wm2
are likely related to the membrane recovery variables, which mimic the refractory properties of neurons that help to reset the membrane potential post-spike.sm
): The variables sm1
and sm2
represent synaptic gating variables, which indicate the fraction of open synaptic channels as a result of spikes influencing neurotransmitter release. These affect how neurons in one population influence those in another.tsyn
): The parameter tsyn
represents the time constant of synaptic transmission, dictating the rate at which the synaptic gating variables open and close.gsyn11
, gsyn12
, gsyn21
, and gsyn22
define the strengths of synapses between and within the neuronal populations, critical for determining how robustly one population can drive activity in another.alpha
, hw
, and constants related to nonlinear dynamics suggest the model examines the stability and change in firing patterns as parameters vary, capturing phenomena like synchronization and oscillations within neuronal networks.The code models the collective behavior of two populations of neurons using the Izhikevich framework, focusing on the dynamic interactions and synaptic integration governed by simplified mean-field principles. It aims to explore neural dynamics under different synaptic, external current, and intrinsic condition variations, reflecting important features like firing patterns and potential emergent network behaviors seen in real neuronal systems.