Kowaliw T., Bredeche N., Doursat R. (eds.) Growing Adaptive Machines. Combining Development and Learning in Artificial Networks

Springer, 2014, -266 p.It is our conviction that the means of construction of artificial neural network topologies is an important area of research. The value of such models is potentially vast. From an applied viewpoint, identifying the appropriate design mechanisms would make it possible to address scalability and complexity issues, which are recognized as major concerns transversal to several communities. From a fundamental viewpoint, the important features behind complex network design are yet to be fully understood, even as partial knowledge becomes available, but scattered within different communities.
Unfortunately, this endeavour is split among different, often disparate domains. We started a workshop in the hope that there was significant room for sharing and collaboration between these researchers. Our response to this perceived need was to gather like-motivated researchers into one place to present both novel work and summaries of research portfolio.Artificial Neurogenesis: An Introduction and Selective Review
A Brief Introduction to Probabilistic Machine Learning and Its Relation to Neuroscience
Evolving Culture Versus Local Minima
Learning Sparse Features with an Auto-Associator
HyperNEAT: The First Five Years
Using the Genetic Regulatory Evolving Artificial Networks (GReaNs) Platform for Signal Processing, Animat Control, and Artificial Multicellular Development
Constructing Complex Systems via Activity-Driven Unsupervised Hebbian Self-Organization
Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural Networks
Artificial Evolution of Plastic Neural Networks: A Few Key Concepts