Computational neurogenetic modeling (CNGM) is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biology, as well as engineering.

Models of the kinetics of proteins and ion channels associated with neuron activity represent the lowest level of modeling in a computational neurogenetic model. The altered activity of proteins in some diseases, such as the amyloid beta protein in Alzheimer's disease, must be modeled at the molecular level to accurately predict the effect on cognition. Ion channels, which are vital to the propagation of action potentials, are another molecule that may be modeled to more accurately reflect biological processes. For instance, to accurately model synaptic plasticity (the strengthening or weakening of synapses) and memory, it is necessary to model the activity of the NMDA receptor (NMDAR). The speed at which the NMDA receptor lets Calcium ions into the cell in response to Glutamate is an important determinant of Long-term potentiation via the insertion of AMPA receptors (AMPAR) into the plasma membrane at the synapse of the postsynaptic cell (the cell that receives the neurotransmitters from the presynaptic cell).

In most models of neural systems neurons are the most basic unit modeled. In computational neurogenetic modeling, to better simulate processes that are responsible for synaptic activity and connectivity, the genes responsible are modeled for each neuron.

A gene regulatory network, protein regulatory network, or gene/protein regulatory network, is the level of processing in a computational neurogenetic model that models the interactions of genes and proteins relevant to synaptic activity and general cell functions. Genes and proteins are modeled as individual nodes, and the interactions that influence a gene are modeled as excitatory (increases gene/protein expression) or inhibitory (decreases gene/protein expression) inputs that are weighted to reflect the effect a gene or protein is having on another gene or protein. Gene regulatory networks are typically designed using data from microarrays.

Modeling of genes and proteins allows individual responses of neurons in an artificial neural network that mimic responses in biological nervous systems, such as division (adding new neurons to the artificial neural network), creation of proteins to expand their cell membrane and foster neurite outgrowth (and thus stronger connections with other neurons), up-regulate or down-regulate receptors at synapses (increasing or decreasing the weight (strength) of synaptic inputs), uptake more neurotransmitters, change into different types of neurons, or die due to necrosis or apoptosis. The creation and analysis of these networks can be divided into two sub-areas of research: the gene up-regulation that is involved in the normal functions of a neuron, such as growth, metabolism, and synapsing; and the effects of mutated genes on neurons and cognitive functions.

An artificial neural network generally refers to any computational model that mimics the central nervous system, with capabilities such as learning and pattern recognition. With regards to computational neurogenetic modeling, however, it is often used to refer to those specifically designed for biological accuracy rather than computational efficiency. Individual neurons are the basic unit of an artificial neural network, with each neuron acting as a node. Each node receives weighted signals from other nodes that are either excitatory or inhibitory. To determine the output, a transfer function (or activation function) evaluates the sum of the weighted signals and, in some artificial neural networks, their input rate. Signal weights are strengthened (long-term potentiation) or weakened (long-term depression) depending on how synchronous the presynaptic and postsynaptic activation rates are (Hebbian theory).

The synaptic activity of individual neurons is modeled using equations to determine the temporal (and in some cases, spatial) summation of synaptic signals, membrane potential, threshold for action potential generation, the absolute and relative refractory period, and optionally ion receptor channel kinetics and Gaussian noise (to increase biological accuracy by incorporation of random elements). In addition to connectivity, some types of artificial neural networks, such as spiking neural networks, also model the distance between neurons, and its effect on the synaptic weight (the strength of a synaptic transmission).

For the parameters in the gene regulatory network to affect the neurons in the artificial neural network as intended there must be some connection between them. In an organizational context, each node (neuron) in the artificial neural network has its own gene regulatory network associated with it. The weights (and in some networks, frequencies of synaptic transmission to the node), and the resulting membrane potential of the node (including whether an action potential is produced or not), affect the expression of different genes in the gene regulatory network. Factors affecting connections between neurons, such as synaptic plasticity, can be modeled by inputting the values of synaptic activity-associated genes and proteins to a function that re-evaluates the weight of an input from a particular neuron in the artificial neural network.