The provided code is a computational modeling script that implements a BCM-like synaptic plasticity model within a conductance-based neuronal framework. The model incorporates several biophysical properties to simulate the physiological behavior of a neuron, focusing on synaptic plasticity and the role of specific ion channels and synaptic receptors. ### Key Biological Concepts Modeled: 1. **Synaptic Plasticity:** - The model implements a version of the Bienenstock, Cooper, and Munro (BCM) theory of synaptic plasticity, which describes how synaptic weights (or strengths) are adjusted based on neural activity. This model incorporates a calcium-dependent synaptic plasticity rule following the work of Shouval et al. (2002). - The synaptic weight changes are governed by the accumulation of calcium ions, which are influenced by neuronal activity, particularly the induction frequency (rate of presynaptic spikes). 2. **Ionic Currents:** - **Sodium (Na) and Potassium Currents (KDR, KA):** The model includes sodium (Na) currents for action potential generation and a delayed rectifier potassium current (KDR), as well as a fast transient potassium current (KA) for repolarization and frequency adaptation. - **H Current (Ih):** The model emphasizes the role of the hyperpolarization-activated cation current (Ih), modeled through the h channel. The Ih current is known to contribute to the regulation of the neuron's resting potential and responsiveness, and in this model, it is suggested as a candidate for regulating the sliding modification threshold in BCM-like synaptic learning. 3. **Synaptic Receptors:** - **AMPA and NMDA Receptors:** These glutamate receptors are modeled using the Goldman-Hodgkin-Katz (GHK) equation. They play critical roles in excitatory synaptic transmission. The AMPA receptor mediates fast synaptic responses, while the NMDA receptor is involved in synaptic plasticity due to its calcium permeability and voltage-dependent Mg²⁺ block. 4. **Ion Equilibrium Potentials:** - **Equilibrium Potentials:** The model sets specific equilibrium potentials for sodium (Ena), potassium (Ek), and the h current (Eh), which are essential for the correct simulation of ion movement and the maintenance of the membrane potential. 5. **Morphological and Passive Electrical Properties:** - The model includes a representation of an apical dendrite segment 200 micrometers from the soma, setting the stage for context-specific biophysical dynamics. - Passive properties, such as axial resistance (Ra), membrane resistance (Rm), and membrane capacitance (Cm), determine how electrical signals decay over distance and time within the dendritic tree. ### Experimental Relevance: The code is designed to replicate experimental findings from Narayanan and Johnston (2010), where the h current's modulation of synaptic plasticity thresholds is investigated. The code outputs simulation data that can be compared to experimental results, demonstrating how changes in Ih density can shift the thresholds for long-term potentiation (LTP) and depression (LTD) in a neural network, reflecting biological observations of neuronal excitability modulation and learning processes. Overall, this model captures the intricate interplay between ion channels, synaptic receptors, and neural activity, providing insights into the biophysical basis of synaptic plasticity and learning in the brain.