Editing Computational neuroscience (section)

===Single-neuron modeling===
{{main|Biological neuron models}}
Even a single neuron has complex biophysical characteristics and can perform computations (e.g.<ref>{{cite journal |author=Forrest MD |title=Intracellular Calcium Dynamics Permit a Purkinje Neuron Model to Perform Toggle and Gain Computations Upon its Inputs. |journal=Frontiers in Computational Neuroscience |volume=8 |pages=86 |year=2014 | doi=10.3389/fncom.2014.00086 |pmid=25191262 |pmc=4138505|doi-access=free }}</ref>). Hodgkin and Huxley's [[Hodgkin–Huxley model|original model]] only employed two voltage-sensitive currents (Voltage sensitive ion channels are glycoprotein molecules which extend through the lipid bilayer, allowing ions to traverse under certain conditions through the axolemma), the fast-acting sodium and the inward-rectifying potassium. Though successful in predicting the timing and qualitative features of the action potential, it nevertheless failed to predict a number of important features such as adaptation and [[Shunting (neurophysiology)|shunting]]. Scientists now believe that there are a wide variety of voltage-sensitive currents, and the implications of the differing dynamics, modulations, and sensitivity of these currents is an important topic of computational neuroscience.<ref>{{cite book |author1=Wu, Samuel Miao-sin |author2=Johnston, Daniel |title=Foundations of cellular neurophysiology |publisher=MIT Press |location=Cambridge, Mass |year=1995 |isbn=978-0-262-10053-3 }}</ref>

The computational functions of complex [[dendrites]] are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.<ref>{{cite book |author=Koch, Christof |title=Biophysics of computation: information processing in single neurons |publisher=Oxford University Press |location=Oxford [Oxfordshire] |year=1999 |isbn=978-0-19-510491-2 }}</ref>

There are many software packages, such as  [[GENESIS (software)|GENESIS]] and [[Neuron (software)|NEURON]], that allow rapid and systematic ''in silico'' modeling of realistic neurons. [[Blue Brain]], a project founded by [[Henry Markram]] from the [[École Polytechnique Fédérale de Lausanne]], aims to construct a biophysically detailed simulation of a [[cortical column]] on the [[Blue Gene]] [[supercomputer]].

Modeling the richness of biophysical properties on the single-neuron scale can supply mechanisms that serve as the building blocks for network dynamics.<ref>{{cite journal|author=Forrest MD|year=2014|title=Intracellular Calcium Dynamics Permit a Purkinje Neuron Model to Perform Toggle and Gain Computations Upon its Inputs.|journal=Frontiers in Computational Neuroscience|volume=8|pages=86|doi=10.3389/fncom.2014.00086|pmc=4138505|pmid=25191262|doi-access=free}}</ref> However, detailed neuron descriptions are computationally expensive and this computing cost can limit the pursuit of realistic network investigations, where many neurons need to be simulated. As a result, researchers that study large neural circuits typically represent each neuron and synapse with an artificially simple model, ignoring much of the biological detail. Hence there is a drive to produce simplified neuron models that can retain significant biological fidelity at a low computational overhead. Algorithms have been developed to produce faithful, faster running, simplified surrogate neuron models from computationally expensive, detailed neuron models.<ref>{{cite journal |author=Forrest MD |title=Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster |journal= BMC Neuroscience | volume=16 |issue=27 |pages=27 | date=April 2015 |doi=10.1186/s12868-015-0162-6 |pmid=25928094 |pmc=4417229 |doi-access=free }}</ref>