Titlebook: Computational Systems Neurobiology; N. Le Novère Book 2012 Springer Science+Business Media Dordrecht 2012 computational neurobiology.dynam

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Historical overview of vascular anastomoses, signaling systems often focus solely on biochemical reaction pathways. Models developed from these data implicitly assume that reactions occur in well mixed homogenous environments with instantaneous diffusion. However, if we have any hope of building truly predictive quantitative models, the intri

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Historical overview of vascular anastomoses,the activity of single neurons can be done by mathematical models of various complexity. In this chapter we start with the integrate-and-fire model, and then consider a set of enhancements so as to approach the behaviour of multiple types of real neurons.

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Historical overview of vascular anastomoses, and works well to predict and understand physiological properties. The mark of a successful theory is that people take it for granted and use it casually. Single neuronal models are no longer remarkable: with the theory well in hand, most interesting questions using models have moved to the network

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Historical overview of vascular anastomoses, complex systems we know of. All these factors conspire to make the status, and process, of building models in these areas problematic. Oftentimes modellers make tacit assumptions about their general approach, but we would argue that such assumptions should be explicit, and that establishing sound m

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Historical overview of vascular anastomoses,nsects and mice. By comparison most neuronal network models developed and simulated up to now have been tiny, comprising many orders of magnitude less neurons than their real counterpart, with an even more dramatic difference when it comes to the number of synapses. In this chapter we discuss why an

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https://doi.org/10.1007/978-3-7985-1715-8nectivity make it difficult, if not impossible, to perform realistic simulations of activity at millimetre range or beyond. Furthermore, it is becoming increasingly clear that a range of non-neuronal and stochastic factors influence neuronal excitability, and must be taken into account when developi