The code provided is part of a computational model in neuroscience, focusing on simulating synaptic conductances and their time courses within a neuronal network. Here's the biological basis of the code:
Synaptic Conductance:
gpe
and gpi
, which are representative of excitatory and inhibitory postsynaptic potentials, respectively. These conductances are key to understanding how neurons integrate inputs and produce an output signal or action potential.Gaussian Distribution of Synaptic Inputs:
gpe
and gpi
are calculated using a Gaussian function, suggesting a spatial distribution of synaptic strength or a representation of heterogeneous synaptic input across a spatial continuum (e.g., a dendrite or neuronal field).mu_e
and mu_i
represent the mean positions of excitatory and inhibitory synaptic inputs, sigma_e
and sigma_i
represent the width of the input distribution, and a_e
and a_i
are the amplitudes or maximum conductance values.Temporal Dynamics:
taur_e
, taud_e
, taur_i
, and taud_i
, representing the rise and decay times of the synaptic conductances. These parameters are crucial for modeling the temporal dynamics of synaptic transmission.tvec
, t0
, and t1
suggests a preference for simulating the conductance changes over time, capturing the kinetics of synaptic transmission.Spatial Selection and Activation:
selected_section_e
and selected_section_i
), implying a consideration for spatial specificity in synaptic activation—aligning with the anatomical reality where certain parts of neurons can be more synaptically active, dependent on their synaptic inputs.x_star
, x_bound_val
), indicating a structured approach to stimuli propagation within this network, possibly mimicking sensory input or feedback mechanisms.Relevance Duration:
rel_dur
parameter modulates the duration for which inhibitory synapses remain active if zero_inhib
is not set, reflecting a scenario where active inhibition is either persistent or transient based on simulated conditions.Overall, the code simulates the complex spatiotemporal dynamics of synaptic conductances in a neural network, capturing the intricacies of excitatory and inhibitory interactions within specific neuronal regions. It models how neurons receive, process, and respond to synaptic inputs, which is foundational to understanding neural computation and communication in the brain.