The provided code models an artificial cell type, known as an AACell (most likely representing an axo-axonic cell, or chandelier cell) within a neural network. The model is inspired by previous work, particularly references to the Santhakumar et al. 2005 model, which suggests a focus on hippocampal microcircuits, and is tailored to replicate different dendritic geometries.
radProx
, radMed
, radDist
) and branches connected to more distal dendrites (lmM
, lmt
). This mirrors the complex structure of axo-axonic cells, which have long, distinct, branched dendrites.oriProx
, oriMed
, oriDist
), recognizing the spatial distribution and potential functional diversity found in real neurons.The biophysical properties include sodium (Na⁺) and potassium (K⁺) channels, reflecting the ion channels responsible for action potential generation and propagation in neurons:
gnatbar_ichan2aa
): Essential for the initiation and propagation of action potentials.gkfbar_ichan2aa
): Involved in repolarization and regulation of neuron excitability.Calcium Dynamics:
nca
, lca
) which are crucial for neurotransmitter release and various cellular signaling pathways.gskch
) further modulate excitability, affecting afterhyperpolarization following action potentials.lmM
, radMed
, and oriProx
, simulating input from excitatory pathways (e.g., entorhinal cortex - EC, CA3 Schaffer collaterals).oriProx
, representing inputs from inhibitory interneurons (e.g., basket cells, Bistratified cells, Septal inputs).Ra
) and membrane capacitance (cm
), integral for determining passive electrical properties.enat
, ek
, enca
) suggest a typical ionic gradient across neural membranes, essential for action potential dynamics and synaptic transmission.This model of an AACell focuses on simulating the electrotonic properties and synaptic inputs to a representative chandelier cell in the hippocampus. The structure, ion channel dynamics, and synaptic architecture reflect the complexities of real neurons, enabling simulation of both excitatory and inhibitory signals that neurons handle. This computational abstraction is critical for understanding the role of axo-axonic cells in modulating neural circuit activity, particularly in the hippocampus, which is vital for processes such as learning and memory.