# Biological Basis of the Code The provided code models synaptic transmission processes involving AMPA and NMDA receptors in the context of presynaptic short-term plasticity. Here’s a breakdown of the biological concepts that are encapsulated within this computational model: ## Synaptic Receptors ### AMPA and NMDA Receptors - **AMPA Receptors**: These are ionotropic glutamate receptors that mediate fast synaptic transmission in the central nervous system. When glutamate is released from the presynaptic neuron and binds to AMPA receptors on the postsynaptic membrane, these receptors open to allow sodium (Na⁺) ions to flow into the neuron, generating an excitatory postsynaptic potential (EPSP). - **NMDA Receptors**: These receptors also bind glutamate but have additional modulatory roles compared to AMPA. They are permeable to sodium (Na⁺), calcium (Ca²⁺), and to a lesser extent, potassium (K⁺). Calcium entry through NMDA receptors is critically important for synaptic plasticity mechanisms, which underlie learning and memory. ### Dual-exponential Profile - The code uses a dual-exponential function to model the conductance change of these receptors following glutamate binding, providing a mathematical representation of how the conductance rises and falls over time (`tau_r` and `tau_d`). This mirrors the rapid activation and slower deactivation of synaptic currents observed in AMPA and NMDA receptors. ## Short-term Synaptic Plasticity ### Facilitation and Depression - **Facilitation (Fac)**: A temporary increase in synaptic strength that occurs due to the accumulation of residual calcium in the presynaptic terminal, which increases the probability of neurotransmitter release during subsequent action potentials. - **Depression (Dep)**: A reduction in synaptic strength due to the depletion of readily releasable neurotransmitter vesicles upon successive presynaptic activation. ### Utilization of Synaptic Efficacy (Use) - This parameter (`Use`) represents the probability that a synapse will release neurotransmitter in response to a presynaptic spike. It highlights how synaptic efficiency can dynamically change in response to ongoing activity. ## Probabilistic Release and Vesicle Dynamics ### Vesicle Release Probability and Dynamics - The model incorporates a mechanism to simulate the probabilistic nature of neurotransmitter release, governed by factors such as vesicle availability (`Pv`) and the facilitation variable (`u`). - The dual-state variables `A` and `B` support the representation of conductance changes in response to synaptic events, and their updates reflect real-time synaptic responses to incoming spikes. - The presynaptic release model implements a vesicle resource pool and calculates release probability (`Pr`) by integrating facilitation and depression effects. ## Miscellaneous ### Synapse Grouping - The model allows for grouping multiple synapses (`Nsyns`) which can be independently triggered, simulating the complex nature of real synaptic networks where multiple synapses can converge onto a single neuron, thus emphasizing the heterogeneity and variability of synaptic inputs. In summary, this code simulates the dynamic interplay of receptor-mediated conductance and presynaptic plasticity mechanisms, which are fundamental for synaptic transmission and plasticity within neural circuits. It provides a computational representation of key biological processes that support neuronal communication and adaptability.