Month: December 2019

A Genetic Model of the Connectome

The connectomes of organisms of the same species show remarkable architectural and often local wiring similarity, raising the question: where and how is neuronal connectivity encoded? Here, we start from the hypothesis that the genetic identity of neurons guides synapse and gap-junction formation and show that such genetically driven wiring predicts the existence of specific biclique motifs in the connectome. We identify a family of large, statistically significant biclique subgraphs in the connectomes of three species and show that within many of the observed bicliques the neurons share statistically significant expression patterns and morphological characteristics, supporting our expectation of common genetic factors that drive the synapse formation within these subgraphs. The proposed connectome model offers a self-consistent framework to link the genetics of an organism to the reproducible architecture of its connectome, offering experimentally falsifiable predictions on the genetic factors that drive the formation of individual neuronal circuits.


A Genetic Model of the Connectome
Dániel L. Barabási, Albert-László Barabási



Machine learning and the physical sciences

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.


Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová
Rev. Mod. Phys. 91, 045002


International Journal of Complexity in Education

The International Journal of Complexity in Education, IJCE, is a new forum which publishes articles that are concerned with the application of complexity theory and related models in the field of education. The journal invites empirical papers, as well as theoretical and methodological contributions, literature reviews and short research reports. We also welcome book reviews. In each of these instances, however, the linkage between complexity theory and education needs to be made explicit, and it should be clear how the contribution adds to existing

knowledge in that area. IJCE is a peer reviewed open source journal. There are no publication charges.


The inaugural issue is planned for April 2020.
To be considered for inclusion in this first issue, send full papers by December 31, 2019.


How to Decide: Simple Tools for Making Better Choices: Annie Duke

Through a blend of compelling exercises, illustrations, and stories, the bestselling author of Thinking in Bets will train you to combat your own biases, address your weaknesses, and help you become a better and more confident decision-maker.


What do you do when you’re faced with a big decision? If you’re like most people, you probably make a pro and con list, spend a lot of time obsessing about decisions that didn’t work out, get caught in analysis paralysis, endlessly seek other people’s opinions to find just that little bit of extra information that might make you sure, and finally go with your gut.

What if there was a better way to make quality decisions so you can think clearly, feel more confident, second-guess yourself less, and ultimately be more decisive and be more productive?

Making good decisions doesn’t have to be a series of endless guesswork. Rather, it’s a teachable skill that anyone can sharpen. In How to Decide, bestselling author Annie Duke and former professional poker player lays out a series of tools anyone can use to make better decisions. You’ll learn:

• To identify and dismantle hidden biases.
• To extract the highest quality feedback from those whose advice you seek.
• To more accurately identify the influence of luck in the outcome of your decisions.
• When to decide fast, when to decide slow, and when to decide in advance.
• To make decisions that more effectively help you to realize your goals and live your values.

Through practical exercises and engaging thought experiments, this book helps you analyze key decisions you’ve made in the past and troubleshoot those you’re making in the future. Whether you’re picking investments, evaluating a job offer, or trying to figure out your romantic life, this book is the key to happier outcomes and fewer regrets.