The provided code is a computational model in Python designed to simulate synaptic plasticity, specifically long-term potentiation (LTP) and long-term depression (LTD), which are critical mechanisms for learning and memory in the brain. The model appears to replicate a "plasticity pairing protocol," which involves the pairing of pre- and postsynaptic activities to induce changes in synaptic strength. Here are the biological underpinnings of the code: ### Synaptic Plasticity - **Spike Timing-Dependent Plasticity (STDP):** The code models STDP, a type of synaptic plasticity where the timing difference between presynaptic and postsynaptic spikes (Δt) influences whether synapses are strengthened (potentiation) or weakened (depression). The `delta_t_range` variable represents different timing intervals, both positive and negative, to explore how the sign and magnitude of Δt affect synaptic strength. ### Synapse and Neuronal Model - **Synaptic Conductance and Gating Variables:** The model uses mechanisms from the NEURON simulation environment (e.g., `ExpSynSTDP` and `ExpSynCaSTDP`) to describe synaptic conductance and plasticity. The `create_syn` function details synaptic parameters like threshold, maximum weight (`w_max`), and potentiation and depression factors, which regulate how synaptic efficacy changes over time based on activity. - **Current Injection and Spiking:** The code uses a current injection to the postsynaptic neuron (`POST_AMP`) to elicit action potentials, simulating post-synaptic spikes required for plasticity induction. ### Ion Channels and Calcium Dynamics - **Calcium Ions (Ca²⁺) and Plasticity:** Calcium signaling is crucial for STDP, where the code uses calcium-dependent models like `ExpSynCaSTDP`. Changes in calcium concentration within dendritic spines are a central trigger for synaptic plasticity. Calcium dynamics are likely involved here given the reference to calcium (`ca_thresh`) in the parameters. - **Ion Channel Gating Variables:** The usage of gating variables (`it2m`, `it2h`, `scam`, `scah`, `kcan`, etc.) for ion channels such as T-type calcium channels and SK channels (`kca`) indicates that the code simulates ionic currents and neuronal excitability. These channels influence neuronal firing patterns and plasticity by modulating membrane potential and calcium entry. ### Inhibition and Shunting - **Shunting Inhibition:** The model includes parameters for inhibitory synapses (`shunt_pos` and related variables), which provide additional layers of complexity by controlling excitatory inputs' effects on the neuron. Inhibition modulates the timing and outcome of LTP/LTD by affecting the postsynaptic neuron's response to inputs. ### Neuronal Architecture - **Dendritic Compartmentalization:** The code refers to different neuronal compartments (e.g., `basal_main`, `oblique_branch`) that reflect the neuron's morphologically distinct regions, each contributing to synaptic integration and plasticity in distinct ways. In summary, the code simulates a detailed neuronal model that incorporates key processes underlying synaptic plasticity, including spike timing, synaptic weights, ion channel dynamics, and calcium signaling. It provides insights into the computational mechanisms of learning and memory by examining how pre- and postsynaptic activities are coordinated to produce lasting changes in synaptic strength.