The provided code is part of a computational neuroscience model, specifically a mean-field model, which attempts to simulate the dynamics of neuronal populations. This code incorporates several fundamental aspects of neuronal biology and seeks to capture the interactions between different types of neurons in a network.
The code models two distinct types of neurons: 'RS-cell' (regular spiking) and 'FS-cell' (fast spiking). These correspond to the typical pyramidal neurons and interneurons, respectively, commonly found in the cortex. Pyramidal neurons are usually excitatory, while fast-spiking neurons are typically inhibitory and play critical roles in balancing excitation and regulating the timing of neuronal activity.
The model includes a synaptic matrix representing the connectivity between neurons within the network (denoted as 'NTWK'). The synaptic interactions are fundamental for neuron communication, affecting both the excitation and inhibition within the network. Excitatory synapses usually involve glutamate as a neurotransmitter, while inhibitory synapses often involve GABA.
The ext_drive refers to external input to the network, which simulates afferent (incoming) signals from other brain areas or sensory systems. The afferent_exc_fraction parameter modulates the proportion of excitatory input from external sources, which affects the overall network dynamics.
Transfer functions (TF1, TF2) are used to model the input-output relationship of neurons, translating synaptic input into neuronal firing rates. These functions capture neuronal responses to varying inputs, including how firing rates are affected by factors such as synaptic weights and external drives.
The model calculates variables such as membrane potential mean (muV), variance (sV), conductance (muGn), and noise time constant (TvN), which are characteristic of the neuron's fluctuation regime. These variables represent the intrinsic properties of neurons influenced by synaptic activity and external inputs, important for understanding neuronal excitability and stability.
The dynamics of the neuronal populations are represented through differential equations, which describe the temporal evolution of firing rates and other state variables. These equations are rooted in biophysical mechanisms governing neuron behavior, such as voltage-gated ion channels that drive action potentials based on membrane potential fluctuations.
Overall, the code implements a mean-field approximation of neuronal activity in a cortical network, combining excitatory and inhibitory interactions, synaptic inputs, and neuronal response dynamics. By capturing the essential features of neurobiological systems, the model provides insights into how network interactions and external inputs influence the neuronal population activity.