The code provided implements a computational model of a pyramidal neuron, specifically a deep layer prefrontal cortex (PFC) pyramidal cell. This type of neuron is crucial for higher cognitive functions and is known to be involved in tasks related to working memory, attention, and decision-making. The model aims to capture the electrical properties and dynamics of such a neuron by simulating its morphology and ion channel distributions. The code is based on earlier models by Durstewitz et al. (2000) and Papoutsi et al. (2014). ### Key Biological Elements #### Neuronal Morphology 1. **Compartmental Modeling**: - The neuron is divided into several compartments: soma (cell body), three dendritic sections (basal, proximal, and distal), and an axon. - These compartments mirror the structural components of a real pyramidal neuron, allowing localized variations in electrical properties that influence signal processing. 2. **Dendritic Structures**: - Each dendritic section has unique properties reflecting differences in structure and function across the dendritic tree. - **Basal dendrites** generally process and integrate local synaptic inputs, while **proximal** and **distal apical dendrites** play roles in integrating long-range inputs and back-propagating action potentials. #### Ion Channel Dynamics The model includes several different types of ion channels, each with distinct functions and permeabilities: - **Sodium Channels (Naf, Nap)**: - `Naf` channels are fast voltage-gated sodium channels vital for action potential initiation. - `Nap` channels represent persistent sodium currents, contributing to subthreshold neuronal excitability and potential bursting behavior. - **Calcium Channels (cal, can, car, cat)**: - These channels regulate calcium influx, impacting synaptic plasticity, neurotransmitter release, and various intracellular signaling pathways. - Different types (`cal`, `can`, `car`, `cat`) reflect diverse localization and function, influencing neuronal excitability and calcium dynamics. - **Potassium Channels (kdr, IKs, kad, iC, kca)**: - Diverse potassium channels regulate repolarization after an action potential and maintain resting membrane potential. - The `kdr` channel controls delayed rectifier currents, significant for action potential termination, while `IKs`, `kad`, `iC`, `kca` modulate other aspects of excitability and firing patterns. - **H Current (h)**: - These represent hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, contributing to the rhythmic activity and stabilization of resting membrane potential. - **Ion Accumulation Dynamics (cadyn)**: - Reflects dynamic changes in intracellular calcium concentration, vital for coupling electrical activity with biochemical signaling. #### Membrane Properties - **Passive Properties**: - Inclusion of passive leak channels (`pas`) allows for the setting of baseline permeability, which, together with active channels, reproduces the neuron's resting potential and resistance. - The capacitance (`cm`) and axial resistivity (`Ra`) reflect the membrane's ability to store and transmit charge, affecting the rate and proportion of potential spread. ### Physiological Conditions - Extracellular and intracellular ionic concentrations for calcium (`cao0_ca_ion`, `cai0_ca_ion`) and potassium (`ko0_k_ion`, `ki0_k_ion`) are set to typical physiological levels to accurately simulate the ionic gradients that drive neuronal activity. - The model temperature (`celsius`) is set to 34°C, approximating the physiological temperature of a rodent, reflecting the typical experimental conditions for such models. By integrating these elements, the model aims to replicate key functional aspects of deep layer PFC pyramidal neurons, providing insights into their contributions to cognitive processes and their response to stimuli.