New England Complex Systems Institute

About: New England Complex Systems Institute is a education organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 95 authors who have published 240 publications receiving 9452 citations. The organization is also known as: NECSI.

Dynamics Of Complex Systems

Abstract: Overview: The Dynamics of Complex Systems-Examples, Questions, Methods and Concepts Introduction and Preliminaries Neural Networks I: Subdivision and Hierarchy Neural Networks II: Models of Mind Protein Folding I: Size Scaling of Time Protein Folding II: Kinetic Pathways Life I: Evolution-Origin of Complex Organisms Life II: Developmental Biology-Complex by Design Human Civilization I: Defining Complexity Human Civilization II: A Complex(ity) Transition.

Cell Fates as High-Dimensional Attractor States of a Complex Gene Regulatory Network

Abstract: Cells in multicellular organisms switch between distinct cell fates, such as proliferation or differentiation into specialized cell types. Genome-wide gene regulatory networks govern this behavior. Theoretical studies of complex networks suggest that they can exhibit ordered (stable) dynamics, raising the possibility that cell fates may represent high-dimensional attractor states. We used gene expression profiling to show that trajectories of neutrophil differentiation converge to a common state from different directions of a 2773-dimensional gene expression state space, providing the first experimental evidence for a high-dimensional stable attractor that represents a distinct cellular phenotype.

The Statistical Mechanics of Complex Product Development: Empirical and Analytical Results

Abstract: In recent years, understanding the structure and function of complex networks has become the foundation for explaining many different real-world complex biological, technological, and informal social phenomena. Techniques from statistical physics have been successfully applied to the analysis of these networks, and have uncovered surprising statistical structural properties that have also been shown to have a major effect on their functionality, dynamics, robustness, and fragility. This paper examines, for the first time, the statistical properties of strategically important organizational networks---networks of people engaged in distributed product development (PD)---and discusses the significance of these properties in providing insight into ways of improving the strategic and operational decision making of the organization. We show that the structure of information flow networks that are at the heart of large-scale product development efforts have properties that are similar to those displayed by other social, biological, and technological networks. In this context, we also identify novel properties that may be characteristic of other information-carrying networks. We further present a detailed model and analysis of PD dynamics on complex networks, and show how the underlying network topologies provide direct information about the characteristics of these dynamics. We believe that our new analysis methodology and empirical results are also relevant to other organizational information-carrying networks.

Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex.

Abstract: Balance of excitation and inhibition is a fundamental feature of in vivo network activity and is important for its computations. However, its presence in the neocortex of higher mammals is not well established. We investigated the dynamics of excitation and inhibition using dense multielectrode recordings in humans and monkeys. We found that in all states of the wake-sleep cycle, excitatory and inhibitory ensembles are well balanced, and co-fluctuate with slight instantaneous deviations from perfect balance, mostly in slow-wave sleep. Remarkably, these correlated fluctuations are seen for many different temporal scales. The similarity of these computational features with a network model of self-generated balanced states suggests that such balanced activity is essentially generated by recurrent activity in the local network and is not due to external inputs. Finally, we find that this balance breaks down during seizures, where the temporal correlation of excitatory and inhibitory populations is disrupted. These results show that balanced activity is a feature of normal brain activity, and break down of the balance could be an important factor to define pathological states.

Global Pattern Formation and Ethnic/Cultural Violence

Abstract: We identify a process of global pattern formation that causes regions to differentiate by culture. Violence arises at boundaries between regions that are not sufficiently well defined. We model cultural differentiation as a separation of groups whose members prefer similar neighbors, with a characteristic group size at which violence occurs. Application of this model to the area of the former Yugoslavia and to India accurately predicts the locations of reported conflict. This model also points to imposed mixing or boundary clarification as mechanisms for promoting peace.