Construction of large-scale neural circuits involves a trade-off between the complexity of the component neurons and the number of neurons that can be represented and simulated given finite computational resources. When one is primarily investigating network- level effects, it is advantageous to choose a simple representation for single neurons. However, single neurons should not be too simple, as interesting effects may occur in the interplay between cellular and network levels. In my talk, I will discuss how a simple cellular model, the integrate-and-fire neuron, can be modified to capture many important cellular properties, while still maintaining low computational expense.
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