The provided code models fluctuating synaptic conductances in a computational neuroscience context. It simulates the effects of synaptic bombardment in neurons, which arises from the random, ongoing activity of synaptic inputs that the neuronal membrane receives.
Synaptic Bombardment:
Excitatory and Inhibitory Conductances:
g_e
) and inhibitory (g_i
). Each has its own reversal potential, E_e
for excitatory and E_i
for inhibitory synapses. Excitatory synapses typically depolarize the neuron (e.g., closer to 0 mV), while inhibitory synapses hyperpolarize it (e.g., closer to -75 mV).Ornstein-Uhlenbeck (OU) Process:
tau_e
and tau_i
are time constants that define how fast the conductances decay to their mean (g_e0
and g_i0
). These represent the synaptic time course dynamics, capturing the brief and transient nature of synaptic events.Stochastic Dynamics:
noise
), reflecting the random fluctuations in synaptic input. These inputs are controlled by noise diffusion coefficients (D_e
and D_i
), which relate to the variance in synaptic conductance.Parameters Relating to Synaptic Conductances:
g_e0
, g_i0
) reflect typical levels of excitatory and inhibitory input the neuron might receive.std_e
, std_i
) indicate the variability around these mean conductances, embodying the idea that synaptic inputs are not constant but fluctuate over time.The model references the study by Destexhe et al., which emphasizes recreating in-vivo-like activity in neocortical neurons by simulating conductances that vary due to synaptic activity. This is aligned with understanding and mimicking the natural biological variability seen in neuronal firing patterns in response to synaptic input.
Overall, the code is a sophisticated representation of synaptic dynamics, providing a platform for studying how complex patterns of synaptic input influence neuronal behavior in a biologically realistic manner.