The provided code is designed to model synaptic transmission and plasticity, primarily focusing on excitatory postsynaptic potentials (EPSPs) in a computational model of a neuronal network. The code encompasses aspects of synaptic dynamics, passive and active neuronal properties, and the integration of different types of excitatory synapses, reflecting their influence on neuronal activity. ### Biological Basis #### Synaptic Conductances - **Passive and Active Conductances**: The model establishes a resting membrane potential (`Vrest = -70 mV`) and inserts both passive and active conductances. Passive conductances refer to the basic ion flow that defines the resting state of the neuron, while active conductances involve voltage-gated ion channels critical for action potential generation and propagation. #### Synapse Dynamics - **NAratio and Synaptic Currents**: The parameter `NAratio = 0.2` specifies the NMDA-to-AMPA receptor ratio. AMPA receptors mediate fast synaptic transmission, while NMDA receptors are involved in slower synaptic potentials and are crucial for synaptic plasticity. - **AMPA and NMDA Properties**: The rise (`atau`) and decay (`ntau`) times characterize the time course of synaptic currents, crucial in shaping EPSPs. AMPA receptors (`atau = 1.5 ms`) have rapid kinetics, whereas NMDA receptors (`ntau = 16 ms`) have longer decay times contributing to synaptic strength and plasticity. #### Synaptic Plasticity - **Release Probability and Facilitation**: Parameters like `p0` (basal release probability) and `Af` (facilitation factor) simulate short-term synaptic plasticity. Facilitation manifests as an increase in neurotransmitter release probability following prior action potentials (`postAP facilitation`), a key feature in learning and memory. - **Recovery and Facilitation Decay**: `tauD` (recovery time from receptor resource pool depletion) and `tauF` (facilitation decay time) simulate synaptic resource availability and short-term adaptations in synaptic strength, respectively. #### Multiple Synaptic Inputs - **NetStim and Synaptic Weighting**: The `NetStim` object provides a synaptic input stimulus, simulating presynaptic action potentials. The weights (`wt`) for different synaptic pathways—mossy fibers (MF), associational/commisural fibers (AC), and perforant path (PP)—represent the contribution of each input to postsynaptic responses. - **Adaptive Synaptic Weighting**: The model iteratively runs simulations with varying synaptic weights (`wtstep`) for the PP synapse, reflecting biological phenomena such as synaptic potentiation or depression over multiple synaptic inputs. ### Conclusion This code models aspects of synaptic function and neural excitability fundamental to understanding neuronal communication and plasticity. It integrates both fast excitatory neurotransmission and the modulatory influence of synaptic plasticity, providing a computational framework for exploring how changes at individual synapses affect overall neuronal output and network dynamics. This is crucial for understanding mechanisms underlying learning and memory in the brain.