Biological Basis of the Model

The code provided is a model of synaptic transmission incorporating two key types of glutamate receptors: AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptors and NMDA (N-methyl-D-aspartate) receptors, alongside mechanisms for presynaptic short-term plasticity. This model is implemented in the NEURON simulation environment, a popular tool for simulating neurons and networks of neurons.

Key Components

AMPA and NMDA Receptors

• AMPA Receptors: AMPA receptors mediate fast excitatory synaptic transmission in the central nervous system. In the model, they are characterized by a dual-exponential conductance profile, where tau_r and tau_d represent the rise and decay time constants, respectively, of the receptor's conductance. AMPA receptors respond quickly, causing rapid depolarization upon glutamate binding due to Na⁺ influx.

• NMDA Receptors: NMDA receptors are also glutamate-gated ion channels but have unique properties, such as voltage-dependency and slower kinetics compared to AMPA receptors. Their conductance includes not just the opening upon glutamate binding but also requires depolarization to alleviate Mg²⁺ block. In the model, the same conductance profile parameters are used for both AMPA and NMDA receptors, simplifying their activation into a combined effect. The reversal potential e is set at 0 mV, typical for excitatory post-synaptic potentials.

Short-term Synaptic Plasticity

• Presynaptic Plasticity: The model incorporates mechanisms for short-term plasticity, which refers to temporary increases or decreases in synaptic strength due to recent activity. This is implemented using parameters and state variables such as:

  • Use: Reflects the utilization of synaptic efficacy upon a presynaptic action potential. It is the probability of vesicle release.
  • Dep (Depression): Describes the time constant for recovery from synaptic depression, where vesicle depletion reduces neurotransmitter release.
  • Fac (Facilitation): Describes the time constant for synaptic facilitation, where increased probability of release occurs due to residual calcium in the presynaptic terminal.

• Vesicle Dynamics: The model includes a computation of available probability (Pv) and release probability (Pr) for synaptic vesicles, adapting from the Fuhrmann et al. 2002 description of vesicle availability and neurotransmitter release patterns at the synapse.

Random Number Generation

• Stochastic Nature: The model incorporates stochastic elements fundamental to biological synaptic transmission, achieved through erand(), to simulate variability in neurotransmitter release. This reflects the inherent randomness of synaptic vesicle release and synaptic transmission.

Overview

This model captures essential elements of synaptic physiology, integrating rapid synaptic currents mediated by AMPA receptors, slow and voltage-dependent NMDA receptor currents, and presynaptic short-term plasticity mechanisms. These features are crucial for understanding synaptic integration, plasticity, and the impact of synaptic dynamics on neural circuit function. The current-voltage relationships (i = g*(v-e)) and normalization factors ensure realistic synaptic conductance behavior, directly influencing computational studies of neural networks and their emergent properties.