# Biological Basis of the Computational Model This code implements a computational model of neuronal networks using the NetPyNE framework, which is designed to facilitate the development and simulation of biologically-realistic neural models. The focus of this particular model is on the EEE (Enhanced, Extended, Experimental) cell types, based on the HH (Hodgkin-Huxley) reduced model for neuronal excitability. ## Neuronal Cell Types and Models The model defines several types of neurons, which are variations of the EEE cell model as well as an SPI6 cell: - **SPI6**: Represents a neuron with reduced complexity, based on the Hodgkin-Huxley model. This cell type likely serves as a control or a baseline for comparison with the more complex EEE models. - **EEE6, EEE7, EEE7us, EEE7ps**: These labels denote different variations of the EEE neuron model. These models differ in their compartmentalization and possible modifications in dendritic geometries and synaptic distributions: - **EEE6**: Consists of 6 compartments with a focus on certain generalized morphological features. - **EEE7**: Extends EEE6 by adding an additional basal dendrite compartment for increased realism in dendritic processing. - **EEE7us and EEE7ps**: Both 7-compartment models that simulate different synaptic spine distributions: - **Uniform Spine Distribution (eee7us)**: Assumes a uniform distribution of spines along dendrites. - **Physiological Spine Distribution (eee7ps)**: Assumes a more realistic distribution based on actual physiological data. ## Network and Synaptic Characteristics - The code specifies synaptic mechanisms, using NMDA and AMPA receptor models. These receptor types are crucial for synaptic transmission and plasticity: - **NMDA Receptors**: These receptors have specific kinetic properties (e.g., `tau1NMDA` and `tau2NMDA`), which are critical for long-term potentiation (LTP), a mechanism related to learning and memory. - **AMPA Receptors**: Represent fast excitatory synaptic transmission, contributing primarily to the initial phase of synaptic response. ## Ionic Conductances and Cable Properties - Ionic conductances are defined for dendritic sections, reflecting realistic neuronal properties: - **nax (Sodium channels)**: The sodium channel conductance (`gbar`) is adjusted, representing the role of sodium in action potential generation and propagation. - **kdr and kap (Potassium channels)**: Potassium channels have their conductances modified via the `cfg.dendK` parameter affecting the after-hyperpolarization phase and neuronal excitability. - **Axial resistance (Ra)**: Different dendritic sections have specified axial resistances (`Ra`), which influence the electrical signaling and integration properties of dendrites. ## External Stimuli and Synaptic Inputs - **IClamp and NetStim inputs**: The model includes mechanisms for injecting current and stochastic synaptic inputs, emulating experimental conditions where neurons receive external currents or reproduce network activity patterns. ## Summary Overall, this code models a small network of interconnected neurons with various morphologies and synaptic mechanisms. Its basic biological aim is to replicate the dynamics of neuronal processing at the cellular level, emphasizing aspects like synaptic transmission, input integration, and cellular excitability patterns influenced by ion channel distributions. These features are central to understanding how neurons communicate and process information in the brain.