Hydrocephalus and Intelligence: The Hollow Men · Gwern.net
Hydrocephalus is a damaging brain disorder where fluids compress the brain, sometimes drastically decreasing its volume. While often extremely harmful or life-threatening when untreated, some people with severe compression nevertheless are relatively normal, and in one case (Lorber) they have been claimed to have IQs as high as 126 with a brain volume 5% of normal brains. An argument recently summarized by ex-biologist SF author argues that intelligence of human brains may have little to do with brain size variables such as neuron count, relying on an example of , a medical condition: For decades now, I have been haunted by the grainy, black-and-white x-ray of a human skull. It is alive but empty, with a cavernous fluid-filled space where the brain should be. A thin layer of brain tissue lines that cavity like an amniotic sac. What scared me was the fact that this virtually brain-free patient had an IQ of 126. Big if true. These claims however run into a lot of objections: Fake data.
Neural Nets Are Overparameterized · Gwern.net
Neural nets are extremely ‘overparameterized’ in the sense that they have orders of magnitude more parameters than necessary to solve the problems they are trained on, as can be proven by the regular improvements in training smaller/faster but still performant networks but also in directly creating smaller neural nets with similar or identical performance on those problems by deleting parameters (sparsification)/reducing precision of the numeric encoding (compressing)/training a much smaller network from scratch using the original large network somehow (distillation); mysteriously, these smaller networks typically cannot be trained from scratch; performance gains can be obtained without the original data; models can be trained to imitate themselves in self-distillation; despite this indicating overfitting ought to be a major concern, they generalize well; and many of these smaller networks are in some sense already present in the original neural network.
The brainweb: Phase synchronization and large-scale integration | Nature Reviews Neuroscience
1Abeles, M. Local cortical circuits (Springer, Berlin, 1982). Google Scholar 2Palm, G. Cell assemblies as a guideline for brain research. Concepts Neurosci. 1, 133–147 ( 1990). Google Scholar 3Eichenbaum, H.
Defecting or Not Defecting: How to “Read” Human Behavior during Cooperative Games by EEG Measurements
Abstract Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity—i.e., the tendency to form two separate subgraphs—than couples playing cooperative or tit-for-tat strategies. Editor: Olaf Sporns, Indiana University, United States of America
the complementary nature book
The Complementary Nature is a genuinely fascinating, provocative, and unique book. It rises to the challenge of describing how either/or thinking obscures the in-between dynamic realities that constitute life itself and in turn how these realities rest on complementary rather than oppositional pairs. In the process, it breaks new ground and opens fresh terrain for future research by illuminating ways in which the science of coordination dynamics—from the study of brains to the study of behavior—offers new paths for understanding the nature of human nature.Maxine Sheets-Johnstoneauthor of The Primacy of Movement To date, Bohr's generalized complementarity principle has been no more than an epistemological stance with little to say to the scientist expressing a normal interest in predicting natural phenomena.
Mathematical Structure IIT.2020
1 Introduction Integrated Information Theory (IIT), developed by Giulio Tononi and collaborators [5, 45–47], has emerged as one of the leading scientific theories of consciousness. At the heart of the latest version of the theory [19, 25, 26, 31, 40] is an algorithm which, based on the level of integration of the internal functional relationships of a physical system in a given state, aims to determine both the quality and quantity (‘ value’) of its conscious experience. While promising in itself [12, 43], the mathematical formulation of the theory is not satisfying to date. The presentation in terms of examples and accompanying explanation veils the essential mathematical structure of the theory and impedes philosophical and scientific analysis. To resolve these problems, we examine the essentials of the IIT algorithm and formally define a generalized notion of Integrated Information Theory. FIGURE 1. Relation to Other Work 1.1 Structure of Article 2 Systems Definition 1. 1. FIGURE 2. 1.
The human brain can create structures in up to 11 dimensions
Neuroscientists have used a classic branch of maths in a totally new way to peer into the structure of our brains. What they've discovered is that the brain is full of multi-dimensional geometrical structures operating in as many as 11 dimensions. We're used to thinking of the world from a 3-D perspective, so this may sound a bit tricky, but the results of this new study could be the next major step in understanding the fabric of the human brain - the most complex structure we know of. This latest brain model was produced by a team of researchers from the Blue Brain Project, a Swiss research initiative devoted to building a supercomputer-powered reconstruction of the human brain. The team used algebraic topology, a branch of mathematics used to describe the properties of objects and spaces regardless of how they change shape. "We found a world that we had never imagined," says lead researcher, neuroscientist Henry Markram from the EPFL institute in Switzerland.
Neurdon
