# Biological Basis of the Computational Model The provided code models a deep layer pyramidal neuron from the prefrontal cortex (PFC) as a multi-compartmental neuron. Pyramidal cells are the principal excitatory neurons in the cerebral cortex and are critically involved in various cognitive functions attributed to the PFC, such as decision-making, attention, and working memory. The code is structured to capture the biophysical properties of these neurons using a computational approach based on compartmental modeling. ## Compartmental Model The model is composed of four compartments: a soma (cell body) and three dendritic compartments. These compartments represent different morphological sections of the neuron, enabling the simulation of electrical behavior across different parts of the cell: - **Soma**: The central compartment, typically where the action potentials are initiated. - **Dend[0] (Basal Dendrite)**: The primary dendritic branch connected to the soma, likely representing basal dendrites. - **Dend[1] (Proximal Dendrite)**: Another dendritic branch, representing proximal apical dendrites near the soma. - **Dend[2] (Distal Dendrite)**: Represents distal apical dendrites, often further from the soma. ## Ion Channels and Conductances Key ionic channels are inserted into each compartment to simulate the neuron's electrical behavior: - **Naf and Nap Channels**: Represent fast and persistent sodium currents, crucial for action potential generation and propagation. - **Hva Channels**: Resemble high-voltage activated calcium channels critical for dendritic processing and synaptic integration. - **kdr and IKs Channels**: Delayed rectifier potassium channels that contribute to repolarizing the action potential and setting the resting membrane potential. - **iC Channels**: Other unspecified ionic conductances that may represent calcium-dependent or other ion channel dynamics. - **cadyn**: A mechanism for calcium dynamics, important for understanding calcium-driven processes such as synaptic plasticity and excitability. The expression of these channels in specific compartments mirrors their distribution across actual neuronal processes, thereby providing a more biologically realistic simulation of neuronal dynamics. ## Passive Properties The model includes passive membrane parameters such as: - **Membrane Capacitance (cm)**: Reflects the capacitive nature of the cell membrane influencing how quickly a cell can respond to a change in voltage. - **Passive Conductance (g_pas)** and **Resting Potential (e_pas)**: Account for the passive leak currents across the membrane, which are vital for maintaining resting membrane potential. ## Ion Concentrations The code also sets the initial concentrations for calcium (`cai0_ca_ion`), potassium (`ko0_k_ion`, `ki0_k_ion`), and extracellular calcium (`cao0_ca_ion`). These concentrations are biologically relevant as they impact the electrochemical gradients driving ion flow through the channels. ## Environmental Conditions - **Temperature (celcius)**: Set to 23 degrees Celsius, a common experimental condition for in vitro neuronal recordings. - **Axial Resistance (Ra)**: Simulates internal resistivity and affects how electrical signals propagate within the neuron. ## Biological Implications This model simulates the electrical activity of a PFC pyramidal neuron, emphasizing action potential propagation, synaptic integration, and the neuron's response to synaptic inputs. This computational model can be used to hypothesize how pyramidal neurons contribute to the computational properties of the PFC, and how alterations in ion channels or synaptic inputs may impact neuronal function or dysfunction, offering insights into diseases like schizophrenia or ADHD that involve the PFC. By realistically representing neuronal morphology and biophysics, these simulations serve as tools to bridge the gap between molecular/cellular neuroscience and systems-level brain function.