Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409261 | Neurocomputing | 2008 | 10 Pages |
Abstract
Virtual neurons are essential in computational neuroscience to study the relation between neuronal form and function. One way of obtaining virtual neurons is by algorithmic generation from scratch. However, a main disadvantage of current available generation methods is that they impose a priori limitations on the outcomes of the algorithms. We present a new tool, EvOL-Neuron, that overcomes this problem by putting a posteriori constraints on generated virtual neurons. We present a proof of principle and show that our method is particularly suited to investigate the neuronal form–function relation.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ben Torben-Nielsen, Karl Tuyls, Eric Postma,