The algorithmic beauty of plants
The algorithmic beauty of plants
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
The NEURON Book
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
| Hi-index | 0.01 |
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.