Chemical processes inside neurons have computational power on their own and occur on a vast range of timescales (from sub-millisecond to weeks). It appears possible that dendritic computation is a significant function that cannot be reduced into a whole-cell model but requires calculation of at least some neuron subsystems.īrain emulation need to take chemistry more into account than commonly occurs in current computational models (Thagard 2002). “Dendritic computing” involves nonlinear interactions in the dendritic tree, allowing parts of neurons to act as ANNs on their own (Single and Borst 1998 London and Hausser 2005 Sidiropoulou, Pissadaki and Poirazi 2006). One of the most important realisations of recent computational neuroscience in recent years is that neurons in themselves hold significant computational resources. In some cases simplifications of morphology can be done based on electrical properties (REF: Rall etc). It is known that the morphology of neurons affects their spiking behaviour (Ascoli 1999), which suggests that neurons cannot simply be simulated as featureless cell bodies. What information does it need for a given resolution? Since then the ability to compute neural activity across large networks has developed enormously thanks to increases in computer power.
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At that point calculating a single action potential using a manually cranked calculator took 8 hours of hard manual work. The area of neural simulation began with the classic Hodgkin and Huxley model of the action potential (Hodgkin and Huxley 1952).
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