The provided code models fluctuating synaptic conductances in a neuronal cell, representing synaptic input from a network of neurons. It captures the biological phenomenon of synaptic bombardment, where a neuron receives continuous, fluctuating synaptic inputs due to the activity of surrounding neurons.
Excitatory and Inhibitory Conductances:
g_e
): Represent synaptic inputs that depolarize the neuron. This is modeled with a reversal potential E_e
of 0 mV, typical for excitatory synapses mediated by neurotransmitters like glutamate.g_i
): Represent synaptic inputs that hyperpolarize the neuron, inhibiting its activity. The reversal potential E_i
is set at -75 mV, simulating inhibition typically mediated by neurotransmitters like GABA.Synaptic Variability:
std_e
) and inhibitory (std_i
) conductances, reflecting biological variability in synaptic strength.Time Constants (tau_e
and tau_i
):
tau_e
) and inhibitory (tau_i
) conductances reflect the kinetics of synaptic currents, with excitatory synaptic inputs typically having shorter durations than inhibitory ones. These constants determine how quickly the conductances can change over time, affecting the integration of synaptic inputs.Stochastic Modeling:
Homeostatic Conductance Baselines (g_e0
and g_i0
):
g_e0
and g_i0
), which represent the mean synaptic input level the neuron experiences in a resting or homeostatic state.Diffusion Coefficients (D_e
and D_i
):
The model captures the essence of fluctuating synaptic inputs that a neuron in a network might experience, with both excitatory and inhibitory components modulated by physiological parameters such as time constants, baseline conductance levels, and stochastic variability. This representation is critical for understanding how neurons integrate synaptic inputs and how this integration influences neuronal excitability and network activity.