The following explanation has been generated automatically by AI and may contain errors.
The code provided aims to model synaptic transmission, focusing specifically on AMPA and NMDA receptor-mediated synaptic currents, and incorporating presynaptic short-term plasticity mechanisms. Here is a breakdown of its biological basis:
### AMPA and NMDA Receptors
1. **Dual-Exponential Conductance Profile**:
- The code models synaptic currents that occur through AMPA and NMDA receptors using a dual-exponential conductance profile. This profile mimics the way actual synaptic current rises and falls over time in biological systems.
- AMPA receptors contribute to fast synaptic transmission, while NMDA receptors mediate slower, longer-lasting synaptic responses and involve calcium permeability critical for synaptic plasticity.
2. **Reversal Potential**:
- Both AMPA and NMDA receptors share a reversal potential in the model, set at 0 mV (`e = 0 mV`). This reflects the non-specific cation permeability of these receptors in biological synapses.
### Short-Term Synaptic Plasticity
The code implements mechanisms to simulate short-term synaptic plasticity based on synaptic efficacy:
1. **Utilization of Synaptic Efficacy (Use)**:
- Represents the initial probability of neurotransmitter release. This can be dynamically adjusted during simulations to reflect variable excitatory post-synaptic potential (EPSP) strength.
2. **Facilitation and Depression**:
- **Facilitation (Fac)**: If greater than 0, it models the tendency for synaptic efficacy to increase when pre-synaptic spikes occur in quick succession. It is implemented as a process where the effective neurotransmitter release probability increases with closely-timed stimuli.
- **Depression (Dep)**: Represents the phenomenon where repeated firing leads to reduced synaptic strength due to depletion of neurotransmitter resources or receptor desensitization, modeled as a relaxation process.
3. **Probability Variables (Pv and Pr)**:
- **Pv**: Probability of a vesicle being available for release, capturing the synaptic resource availability.
- **Pr**: Product of Utilization (`u`) and Vesicle Probability (`Pv`), reflecting the overall probability of neurotransmitter release during an action potential.
### Stochastic Synaptic Release
- The model includes random activation of synapses, using both deterministic and stochastic methods to decide whether a synapse is activated upon a spike. This reflects the probabilistic nature of synaptic transmission at individual synapses.
### Random Number Generation
- The code uses random numbers to simulate variability in synaptic release, crucial for capturing the inherent stochasticity of neurotransmitter release in real synapses. A specific random noise mechanism ensures reproducibility and independence of random sequences across parallel simulations.
This model represents a sophisticated attempt to capture both the temporal dynamics and probabilistic nature of synaptic transmission, leveraging biophysical principles observed in real neural tissues to model short-term synaptic plasticity and the dynamics of receptor activity.