Computational Neuroscience

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The branch of neuroscience using computational research techniques to explore, examine and test the theories and principles of brain functions

Computational Simulation

Computer models of brain functions
applying known theories and equations of the brain, neural networks, and neurons

Computational Analysis

Computer analysis of brain functions
applying mathematical/statistical analyses to neural signals (including electrical, magnetic, chemical, optical signals of the brain, neural networks, and neurons)

The Objectives of Computational Neuroscience Research

Simulation Objectives

To test the theories and principles
by modeling (reconstructing) brain functions at the level of:

* whole brain
* neural networks
* neurons and synapses
* substructures of neurons
* genetic encodings

Analysis Objectives

To deduce brain functions based on
the analyses of the signals and structures at the level of:

* whole brain
* neural networks
* neurons and synapses
* substructures of neurons
* genetic codes

Techniques in Computational Neuroscience Research:

Developing theories of operations of the brain:

* Develop mathematical equations that govern brain functions
* Develop equations that describe electrical, biochemical properties of neurons & neural networks
* Reconstruct biologically-realistic model of neural networks
* Simulate the functions of neural networks based on the model
* Perform experiments to test the model to see if it works as predicted

Developing analyses of the complex functions of the brain:

* Develop analytical techniques to decode the functions of the brain
* Develop metholodologies to analyze brain signals
* Using mathematical/statistical analyses to deduce principles of operation
* Apply analyses to reduce the complexity of the neural system
* Based on the analyses, formulate theories and principles of brain functions