The provided code is a computational model aimed at reproducing the rate dependence of synaptic plasticity as observed in the study by Sjöström et al. (2001), which explored long-term potentiation (LTP) and long-term depression (LTD) in neocortical synapses. Here’s a breakdown of the biological basis underlying this model: ### Synaptic Plasticity The code simulates aspects of synaptic plasticity, specifically focusing on the rate-dependent aspects where different frequencies of neuronal firing influence whether synaptic strength is increased (potentiation) or decreased (depression). This is directly relevant to Hebbian plasticity, a fundamental principle where an increase in the activity of synaptic connections correlates with their strengthening. ### STDP Protocol The code uses spike-timing dependent plasticity (STDP), a biological mechanism where the timing of pre- and postsynaptic spikes determines the direction and magnitude of plasticity. Pre-before-post synaptic spiking can lead to LTP, while post-before-pre spiking can induce LTD. This model involves simulating these pairings at various frequencies, reflecting classical STDP experiments. ### Morphological Considerations The code includes references to two specific neuron morphologies (`Acker2008` and potentially a commented-out `Branco2010`), hinting at detailed spatial modeling of neurons. This accounts for the fact that synaptic inputs can occur in various locations within the dendritic tree, influencing the local calcium dynamics, which are critical for synaptic changes. ### Neuron and Synapse Models - **Integrate-and-Fire Neurons**: The model employs Leaky Integrate-and-Fire (LIF) neurons as input neurons, reflecting neural activity that leads to synaptic changes. This is a simplified model that captures essential membrane properties. - **Calcium Dynamics**: The code initializes neuron models with calcium-dependent mechanisms (`makeNeuron_Ca`). Calcium ions are crucial for synaptic plasticity, as they facilitate signaling pathways that lead to LTP or LTD. - **MgBlock of NMDA Receptors**: The mention of a `Mgblock` indicates the inclusion of NMDA receptor dynamics, where magnesium block is a critical factor for NMDA receptor activation, which requires both ligand binding and postsynaptic depolarization. This dual requirement is essential for calcium influx and subsequent plasticity. ### Synaptic Models - **AMPA Plasticity**: The synaptic model includes equations related to AMPA receptor plasticity (`eq_1_plastAMPA`, `eq_2_plastAMPA`). AMPA receptors mediate fast synaptic transmission and are key targets for synaptic modification in plasticity processes. ### Simulation Protocol The model simulates synaptic changes using a protocol similar to those used in experimental settings to test LTP and LTD under different conditions. The parameter `hz_array` represents the frequency of the spike pairings which are systematically varied to observe synaptic weight changes, a critical parameter studied in the context of STDP. ### Biological Hierarchy Lastly, the code distinguishes between proximal and distal compartments of the neuron, recognizing that synaptic changes can vary based on the synapse's location within the dendritic tree—a significant biological reality that affects the electrical and chemical milieu of the synapse, influencing plasticity outcomes. In summary, this code intricately models key aspects of synaptic plasticity, focusing on how neuronal firing rates influence synaptic strength, guided by experimental observations from Sjöström et al., and integrates detailed neuronal structure and receptor dynamics to realistically simulate potential biological outcomes.