The provided code appears to model aspects of neuronal activity using a computational neuroscience approach. The main biological components captured in this script are described below:
The script references Wilson-Cowan parameters, indicating that it uses elements of the Wilson-Cowan equations, a set of mathematical equations for modeling neuronal dynamics, especially population behavior in neural circuits. The Wilson-Cowan model typically captures the interaction between excitatory and inhibitory populations of neurons.
Synaptic Conductance (p.g0
): The code specifies different peak conductances for synaptic connections (p.g0
), representing the maximum synaptic strength at which neurotransmitters impact post-synaptic neurons. The values in mS/cm² suggest a focus on conductance-based synapses, often modeled in networks to simulate neurotransmitter effects.
Synaptic Time Constants (p.gamma
): The variable p.gamma
holds the reciprocal of synaptic time constants, indicating the temporal dynamics of synaptic responses. These constants affect how quickly synaptic effects rise and decay.
Threshold Potential (p.Vth
), Excitatory (p.Eex
), and Inhibitory Reversal Potentials (p.Ein
): These parameters are essential for defining the biophysical properties of neurons. The threshold potential determines when a neuron fires an action potential, while the reversal potentials represent the voltage at which no net ionic flow occurs for excitatory and inhibitory synapses, respectively.
Inhibitory Synapse Conductance (p.Cinh
): The code calculates an effective conductance for inhibitory synapses, adjusting the conductance for interplay with excitatory synaptic inputs in terms of their electrophysiological impact.
Connectivity Matrices (W2
, W3
, W4
, W5
): These are probabilistic random matrices representing interconnections between neurons, specifically excitatory-to-excitatory, excitatory-to-inhibitory, inhibitory-to-excitatory, and inhibitory-to-inhibitory connections. The statistical properties of these matrices (e.g., mean and variance of synaptic connections) are crucial for approximating the structure and functional connectivity of neural networks.
Synaptic Variability (p.Nsyna
, p.varN
): These variables capture the variability in network connectivity, which can simulate biological heterogeneity among synapses and neuronal connections as seen in real neural circuits.
p.lambda
): This parameter represents the rate of external inputs to the network, often used to model background or stimulus-driven activity that neurons receive from external sources, expressed in Hz (frequency of input events).p.Dt
, p.T
): Simulation parameters manage the temporal resolution and duration of the model's computational experiments, reflecting biological timescales relevant to neural processes.In summary, the code primarily models the dynamics of neural populations with a focus on synaptic interactions and network connectivity. It uses Wilson-Cowan principles to simulate the interplay between excitatory and inhibitory neurons, incorporating realistic synaptic dynamics and network structures seen in biological systems.