The provided code is part of a computational neuroscience model simulating Spike-Timing-Dependent Plasticity (STDP) in cortical neurons. Here are the key biological concepts and structures that are captured in the code: ### Biological Objective The primary goal of this code is to simulate the STDP window for synaptic plasticity at a frequency of 20 Hz in two particular locations within a neuron: distal and proximal compartments. STDP is a biological process whereby the synaptic strength is modified based on the precise timing of spikes between pre- and postsynaptic neurons. This model attempts to replicate how synaptic weights are adjusted depending on the spike intervals in these different neuronal compartments. ### Neuronal Morphology The code uses morphological models of neurons based on anatomical data. Specifically, it includes models derived from *Acker's* and possibly *Branco's* morphological neuron data. These models incorporate realistic neuronal structures from the cortex, focusing on dendritic compartments, which are crucial for input integration and synaptic plasticity. ### Physiological Details - **Membrane Potentials and Thresholds:** The resting potential (`V_rest`), spike threshold (`V_thresh`), and membrane capacitance (`C`) are defined to replicate neuronal electrical properties. - **Input Neurons:** The model comprises input neurons that simulate synaptic input to the dendritic compartments, key for inducing STDP. These input neurons are modeled with simple integrate-and-fire dynamics. - **Calcium Dynamics:** Calcium signaling is implicitly involved through the `makeNeuron_Ca` function, suggesting that changes in calcium concentration are crucial for the activation of STDP. Calcium influx often acts as a secondary messenger in synaptic plasticity, mediating the biochemical reactions leading to changes in synaptic strength. - **Voltage Gating:** The periodic update of `Mgblock`, representing the magnesium block of NMDA receptors, reflects the transition between states that allow calcium influx, which is critical for synaptic plasticity. ### Synaptic Plasticity - **Synapses and Plasticity Mechanisms:** The model incorporates synapses with plasticity rules defined by equations (`eq_1_plastAMPA` and `eq_2_plastAMPA`), which govern how synaptic weights change as a function of pre- and postsynaptic activity. STDP is modeled by varying synaptic weights based on the timing differences of spike pre- and post-neuron spikes. - **Repetition and Interval Variability:** Multiple pairings and varied interspike intervals (as defined in `dt_array`) are simulated to recreate the STDP curve, capturing the nonlinear relationships of synaptic changes based on the timing of action potentials. ### Simulation Environments The simulation accounts for two environments: distal and proximal dendritic compartments. These compartments have different biophysical properties, and their role in information processing varies within the neuron. The differential impact of STDP in these compartments helps to understand how spatial factors influence synaptic learning in neurons. ### Data Representation The code ultimately aims to generate an STDP curve representing how synaptic weight changes depending on the timing of the spikes. These weight changes are normalized to reflect their percentage increase or decrease relative to the initial conditions, providing a detailed view of synaptic modification as induced by various spike timings. In summary, the code models fundamental biological processes of synaptic plasticity, incorporating detailed morphological and physiological neuron data to understand how timing differences in neuronal firing can affect synaptic strength differently in proximal and distal dendritic compartments.