Neural Darwinism is a biological, and more specifically Darwinian and selectionist, approach to understanding global brain function, originally proposed by American biologist, researcher and Nobel-Prize recipient Gerald Maurice Edelman. Edelman's 1987 book Neural Darwinism introduced the public to the theory of neuronal group selection (TNGS) – which is the core theory underlying Edelman's explanation of global brain function.
Stuart Alan Kauffman is an American medical doctor, theoretical biologist, and complex systems researcher who studies the origin of life on Earth. He was a professor at the University of Chicago, University of Pennsylvania, and University of Calgary. He is currently emeritus professor of biochemistry at the University of Pennsylvania and affiliate faculty at the Institute for Systems Biology. He has a number of awards including a MacArthur Fellowship and a Wiener Medal.
In astronomy, the term compact star refers collectively to white dwarfs, neutron stars, and black holes. It would grow to include exotic stars if such hypothetical, dense bodies are confirmed to exist. All compact objects have a high mass relative to their radius, giving them a very high density, compared to ordinary atomic matter.
Self-organized criticality (SOC) is a property of dynamical systems that have a critical point as an attractor. Their macroscopic behavior thus displays the spatial or temporal scale-invariance characteristic of the critical point of a phase transition, but without the need to tune control parameters to a precise value, because the system, effectively, tunes itself as it evolves towards criticality.
Evolvability is defined as the capacity of a system for adaptive evolution. Evolvability is the ability of a population of organisms to not merely generate genetic diversity, but to generate adaptive genetic diversity, and thereby evolve through natural selection.
In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving.
J. Doyne Farmer is an American complex systems scientist and entrepreneur with interests in chaos theory, complexity and econophysics. He is Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment, Oxford University, where he is also director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School. Additionally he is an external professor at the Santa Fe Institute. His current research is on complexity economics, focusing on systemic risk in financial markets and technological progress. During his career he has made important contributions to complex systems, chaos, artificial life, theoretical biology, time series forecasting and econophysics. He co-founded Prediction Company, one of the first companies to do fully automated quantitative trading. While a graduate student he led a group that called itself Eudaemonic Enterprises and built the first wearable digital computer, which was used to beat the game of roulette.
Tang Chao is a Chair Professor of Physics and Systems Biology at Peking University.
Paulien Hogeweg is a Dutch theoretical biologist and complex systems researcher studying biological systems as dynamic information processing systems at many interconnected levels. In 1970, together with Ben Hesper, she defined the term bioinformatics as "the study of informatic processes in biotic systems".
The evolution of biological complexity is one important outcome of the process of evolution. Evolution has produced some remarkably complex organisms – although the actual level of complexity is very hard to define or measure accurately in biology, with properties such as gene content, the number of cell types or morphology all proposed as possible metrics.
An ecological network is a representation of the biotic interactions in an ecosystem, in which species (nodes) are connected by pairwise interactions (links). These interactions can be trophic or symbiotic. Ecological networks are used to describe and compare the structures of real ecosystems, while network models are used to investigate the effects of network structure on properties such as ecosystem stability.
Fractal analysis is assessing fractal characteristics of data. It consists of several methods to assign a fractal dimension and other fractal characteristics to a dataset which may be a theoretical dataset, or a pattern or signal extracted from phenomena including topography, natural geometric objects, ecology and aquatic sciences, sound, market fluctuations, heart rates, frequency domain in electroencephalography signals, digital images, molecular motion, and data science. Fractal analysis is now widely used in all areas of science. An important limitation of fractal analysis is that arriving at an empirically determined fractal dimension does not necessarily prove that a pattern is fractal; rather, other essential characteristics have to be considered. Fractal analysis is valuable in expanding our knowledge of the structure and function of various systems, and as a potential tool to mathematically assess novel areas of study. Fractal calculus was formulated which is a generalization of ordinary calculus.
In the biological sciences, the term bow tie is a recent concept that tries to grasp the essence of some operational and functional structures observed in biological organisms and other kinds of complex and self-organizing systems. In general, bow tie architectures refer to ordered and recurrent structures that often underlie complex technological or biological systems, and that are capable of conferring them a balance among efficiency, robustness and evolvability. In other words, bow ties are able to take into account a great diversity of inputs, showing a much smaller diversity in the protocols and processes able to elaborate these inputs, and finally an extremely heterogeneous diversity of outputs. These architectures thus manage a wide range of inputs through a core (knot) constituted by a limited number of elements. In such structures, inputs are conveyed into a sort of funnel, towards a "synthesis" core, where they can be duly organized, processed and managed by means of protocols, and from where, in turn, a variety of outputs, or responses, is propagated.
Integrated information theory (IIT) attempts to provide a framework capable of explaining why some physical systems are conscious, why they feel the particular way they do in particular states, and what it would take for other physical systems to be conscious. In principle, once the theory is mature and has been tested extensively in controlled conditions, the IIT framework may be capable of providing a concrete inference about whether any physical system is conscious, to what degree it is conscious, and what particular experience it is having. In IIT, a system's consciousness is conjectured to be identical to its causal properties. Therefore it should be possible to account for the conscious experience of a physical system by unfolding its complete causal powers.
In evolutionary biology, robustness of a biological system is the persistence of a certain characteristic or trait in a system under perturbations or conditions of uncertainty. Robustness in development is known as canalization. According to the kind of perturbation involved, robustness can be classified as mutational, environmental, recombinational, or behavioral robustness etc. Robustness is achieved through the combination of many genetic and molecular mechanisms and can evolve by either direct or indirect selection. Several model systems have been developed to experimentally study robustness and its evolutionary consequences.
James P. Crutchfield is an American mathematician and physicist. He received his B.A. summa cum laude in physics and mathematics from the University of California, Santa Cruz, in 1979 and his Ph.D. in physics there in 1983. He is currently a professor of physics at the University of California, Davis, where he is director of the Complexity Sciences Center—a new research and graduate program in complex systems. Prior to this, he was research professor at the Santa Fe Institute for many years, where he ran the Dynamics of Learning Group and SFI's Network Dynamics Program. From 1985 to 1997, he was a research physicist in the physics department at the University of California, Berkeley. He has been a visiting research professor at the Sloan Center for Theoretical Neurobiology, University of California, San Francisco; a postdoctoral fellow of the Miller Institute for Basic Research in Science at UCB; a UCB physics department IBM postdoctoral fellow in condensed matter physics; a distinguished visiting research professor of the Beckman Institute at the University of Illinois, Urbana-Champaign; and a Bernard Osher Fellow at the San Francisco Exploratorium.
A neutral network is a set of genes all related by point mutations that have equivalent function or fitness. Each node represents a gene sequence and each line represents the mutation connecting two sequences. Neutral networks can be thought of as high, flat plateaus in a fitness landscape. During neutral evolution, genes can randomly move through neutral networks and traverse regions of sequence space which may have consequences for robustness and evolvability.
Erik van Nimwegen is a Dutch computational biologist and Professor at the Biozentrum of the University of Basel, Switzerland.
Epistasis is a phenomenon in genetics in which the effect of a gene mutation is dependent on the presence or absence of mutations in one or more other genes, respectively termed modifier genes. In other words, the effect of the mutation is dependent on the genetic background in which it appears. Epistatic mutations therefore have different effects on their own than when they occur together. Originally, the term epistasis specifically meant that the effect of a gene variant is masked by that of a different gene.
Erik Hoel is an American neuroscientist, neurophilosopher and fiction writer. His main areas of research are the study and philosophy of consciousness, cognition, biological function of dreams, and mathematical theories of emergence. He is noted for using information theory and causal analysis to develop mathematical models to explore and understand the basis of consciousness and dreams. Hoel holds a PhD degree in neuroscience from the University of Wisconsin–Madison, and in 2018 was recipient of Forbes 30 Under 30 – Science. He is currently working as a research assistant professor at Tufts University.
