Development and Evolution of Neural Networks in an Artificial Chemistry

We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and cell stimulation. We demonstrate the model's power with a hand-written genome that leads to the growth of a simple network which performs classical conditioning. To evolve more complex structures, we implemented a platform-independent, asynchronous, distributed Genetic Algorithm (GA) that allows users to participate in evolutionary experiments via the World Wide Web.Comment: 8 pages LaTeX, style file included, 8 embedded postscript figures. To be published in Proc. of 3rd German Workshop on Artificial Life (GWAL

A polymorphism associated with depressive disorders differentially regulates brain derived neurotrophic factor promoter IV activity

PMCID: PMC3712170Peer reviewedPublisher PD

Accelerated Evolution of the ASPM Gene Controlling Brain Size Begins Prior to Human Brain Expansion

Primary microcephaly (MCPH) is a neurodevelopmental disorder characterized by global reduction in cerebral cortical volume. The microcephalic brain has a volume comparable to that of early hominids, raising the possibility that some MCPH genes may have been evolutionary targets in the expansion of the cerebral cortex in mammals and especially primates. Mutations in ASPM, which encodes the human homologue of a fly protein essential for spindle function, are the most common known cause of MCPH. Here we have isolated large genomic clones containing the complete ASPM gene, including promoter regions and introns, from chimpanzee, gorilla, orangutan, and rhesus macaque by transformation-associated recombination cloning in yeast. We have sequenced these clones and show that whereas much of the sequence of ASPM is substantially conserved among primates, specific segments are subject to high Ka/Ks ratios (nonsynonymous/synonymous DNA changes) consistent with strong positive selection for evolutionary change. The ASPM gene sequence shows accelerated evolution in the African hominoid clade, and this precedes hominid brain expansion by several million years. Gorilla and human lineages show particularly accelerated evolution in the IQ domain of ASPM. Moreover, ASPM regions under positive selection in primates are also the most highly diverged regions between primates and nonprimate mammals. We report the first direct application of TAR cloning technology to the study of human evolution. Our data suggest that evolutionary selection of specific segments of the ASPM sequence strongly relates to differences in cerebral cortical size

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

A genomic approach to the study of Tribolium castaneum genetics, development & evolution

During the last decade, Tribolium castaneum has become the insect of choice for comparative genetics and developmental studies outside of drosophilids. Until recently, most molecular studies have focused on the comparative analysis of early development with a focus on segmentation and homeotic genes. In order to acquire independent knowledge on the genetic basis of insect development, a genomic approach consisting of EST and BAC-ends sequencing projects has been initiated in Tribolium. The EST project resulted in the production of 2,246 random sequences representing 488 non-redundant EST contigs. Of those, 280 sequences were selected, along with 86 independently cloned putative transcription factors, and further characterized by in situ hybridization. Expression analysis led to the identification of at least 25 novel genes putatively involved in diverse aspects of Tribolium embryonic development such as segmentation, appendage development, neurogenesis, myogenesis and terminal patterning. Comparative evolutionary analysis of the EST sequences verified that Tribolium is a slow evolving species when compared to dipterans. As predicted by the neutral theory, the data also revealed that evolutionary rates are a composite measure of both gene and species specific rates. To date, the BAC-ends sequencing project resulted in the production of 8,640 sequences covering 2.9% of the Tribolium genome. A functional analysis of a subset of these BAC-end sequences (BES) allowed the identification of 486 putative ORFs. It is estimated that of the 53,000 BES to be produced, 6,900 ORFs will be found, comprising 18% of the genome. Random sequencing of ESTs and production of BES are shown to be powerful ways to identify new genes, to help mapping the Tribolium genome and to identify coding regions in genomic sequences

Is it conceivable that neurogenesis, neural Darwinism, and species evolution could all serve as inspiration for the creation of evolutionary deep neural networks?

Deep Neural Networks (DNNs) are built using artificial neural networks. They are part of machine learning methods that are capable of learning from data that have been used in a wide range of applications. DNNs are mainly handcrafted and they usually contain numerous layers. Research frontier has emerged that concerns automated construction of DNNs via evolutionary algorithms. This paper emphasizes the importance of what we call two-dimensional brain evolution and how it can inspire two dimensional DNN evolutionary modeling. We also highlight the connection between the dropout method which is widely-used in regularizing DNNs and neurogenesis of the brain, and how these concepts could benefit DNNs evolution.The paper concludes with several recommendations for enhancing the automatic construction of DNNs