The provided code models synaptic conductance dynamics for excitatory and inhibitory synapses within a computational neuroscience framework. Here is the biological basis relevant to the code:
1. Excitatory Synapses:
Neurotransmitter and Receptors: The excitatory synapses characterized in this model are representative of synapses that use glutamate as the neurotransmitter. Glutamate typically binds to ionotropic receptors such as AMPA and NMDA receptors on the postsynaptic neuron.
Reversal Potential (EGlu): The reversal potential for excitatory postsynaptic currents is set to 0.0 volts (0 mV), which reflects a typical postsynaptic membrane potential where excitatory currents cease to drive depolarization.
Conductance Time Course: The model uses a dual exponential function to represent how synaptic conductances at excitatory synapses change over time following neurotransmitter release. This captures the fast activation and relatively slower deactivation kinetics of glutamatergic synapse-mediated currents.
2. Inhibitory Synapses:
Neurotransmitter and Receptors: The inhibitory synapses modeled here are mediated by GABA (Gamma-Aminobutyric Acid), the primary inhibitory neurotransmitter in the mammalian central nervous system. GABA typically activates GABA_A receptors, which are ionotropic and cause an influx of chloride ions, leading to hyperpolarization of the postsynaptic neuron.
Reversal Potential (EGABA): The reversal potential of -0.08 volts (-80 mV) is reflective of the chloride ion equilibrium potential, typical for GABAergic synapses, which generally creates hyperpolarizing currents that inhibit neuronal firing.
Conductance Time Course: Similar to excitatory synapses, a dual exponential function characterizes how conductances change over time at inhibitory synapses, reflecting the binding and unbinding dynamics of GABA to its receptor.
The code distinguishes between standard synaptic channels (synchan) and intrinsic synaptic channels (isynchan), both presumably describing different dynamics or computational efficiencies within the simulation framework. However, the biological basis is similar, focusing on how synaptic conductance changes influence membrane potential and neuronal firing.
The approach taken in the code reflects important biophysical principles:
Synaptic Current Dynamics: Models the fast synaptic transmission dynamics crucial for understanding neuronal signaling and information processing in neural networks.
Neuron Communication: Highlights the fundamental role of synaptic conductances in modulating synaptic strength and neuronal excitability, mimicking real brain processes such as synaptic integration and plasticity.
Overall, the code captures essential aspects of excitatory and inhibitory synaptic transmission, underlying neuronal computations and network behaviors in biological systems.