Connectome: How the Brains Wiring Makes Us Who We Are Pdf, Epub, Ebook
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Against Transhumanism the Delusion of Technological Transcendence
Edition 1.0 Against Transhumanism The delusion of technological transcendence Richard A.L. Jones Preface About the author Richard Jones has written extensively on both the technical aspects of nanotechnology and its social and ethical implications; his book “Soft Machines: nanotechnology and life” is published by OUP. He has a first degree and PhD in physics from the University of Cam- bridge; after postdoctoral work at Cornell University he has held positions as Lecturer in Physics at Cambridge University and Profes- sor of Physics at Sheffield. His work as an experimental physicist concentrates on the properties of biological and synthetic macro- molecules at interfaces; he was elected a Fellow of the Royal Society in 2006 and was awarded the Institute of Physics’s Tabor Medal for Nanoscience in 2009. His blog, on nanotechnology and science policy, can be found at Soft Machines. About this ebook This short work brings together some pieces that have previously appeared on my blog Soft Machines (chapters 2,4 and 5). Chapter 3 is adapted from an early draft of a piece that, in a much revised form, appeared in a special issue of the magazine IEEE Spectrum de- voted to the Singularity, under the title “Rupturing the Nanotech Rapture”. Version 1.0, 15 January 2016 The cover picture is The Ascension, by Benjamin West (1801). Source: Wikimedia Commons ii Transhumanism, technological change, and the Singularity 1 Rapid technological progress – progress that is obvious by setting off a runaway climate change event, that it will be no on the scale of an individual lifetime - is something we take longer compatible with civilization. -
Spiking Allows Neurons to Estimate Their Causal Effect
bioRxiv preprint doi: https://doi.org/10.1101/253351; this version posted January 25, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Spiking allows neurons to estimate their causal effect Benjamin James Lansdell1,+ and Konrad Paul Kording1 1Department of Bioengineering, University of Pennsylvania, PA, USA [email protected] Learning aims at causing better performance and the typical gradient descent learning is an approximate causal estimator. However real neurons spike, making their gradients undefined. Interestingly, a popular technique in economics, regression discontinuity design, estimates causal effects using such discontinuities. Here we show how the spiking threshold can reveal the influence of a neuron's activity on the performance, indicating a deep link between simple learning rules and economics-style causal inference. Learning is typically conceptualized as changing a neuron's properties to cause better performance or improve the reward R. This is a problem of causality: to learn, a neuron needs to estimate its causal influence on reward, βi. The typical solution linearizes the problem and leads to popular gradient descent- based (GD) approaches of the form βGD = @R . However gradient descent is just one possible approximation i @hi to the estimation of causal influences. Focusing on the underlying causality problem promises new ways of understanding learning. Gradient descent is problematic as a model for biological learning for two reasons. First, real neurons spike, as opposed to units in artificial neural networks (ANNs), and their rates are usually quite slow so that the discreteness of their output matters (e.g. -
Glia and Neurons Glia and Neurons Objective: Role of Glia in Nervous System Agenda: 1
Applied Neuroscience Columbia Science Honors Program Spring 2017 Glia and Neurons Glia and Neurons Objective: Role of Glia in Nervous System Agenda: 1. Glia • Guest Lecture by Dr. Jennifer Ziegenfuss 2. Machine Learning • Applications in Neuroscience Connectomics A connectome is a comprehensive map of neural connections in the brain (wiring diagram). • Fails to illustrate how neurons behave in real-time (neural dynamics) • Fails to show how a behavior is generated • Fails to account for glia A Tiny Piece of the Connectome Serial EM Reconstruction of Axonal Inputs (various colors) onto a section of apical dendrite (grey) of a pyramidal neuron in mouse cerebral cortex. Arrows mark functional synapses. Lichtman Lab (Harvard) Scaling up efforts to map neural connections is unlikely to deliver the promised benefits such as understanding: • how the brain produces memories • Perception • Consciousness • Treatments for epilepsy, depression, and schizophrenia While glia have been neglected in the quest to understand neuronal signaling, they can sense neuronal activity and control it. Are Glia the Genius Cells? Structure-Function Divide in Brain The function of a neural network is critically dependent upon its interconnections. Only C. elegans has a complete connectome. In neuroscience: • many common diseases and disorders have no known histological trace • debates how many cell types there are • questionable plan to image whole volumes by EM • complexity of structure How is the brain’s function related to its complex structure? Worm Connectome Dots are individual neurons and lines represent axons. C. elegans Caenorhabditis elegans (C. elegans) is a transparent nematode commonly used in neuroscience research. They have a simple nervous system: 302 neurons and 7000 synapses. -
UNDERSTANDING the BRAIN Tbook Collections
FROM THE NEW YORK TIMES ARCHIVES UNDERSTANDING THE BRAIN TBook Collections Copyright © 2015 The New York Times Company. All rights reserved. Cover Photograph by Zach Wise for The New York Times This ebook was created using Vook. All of the articles in this work originally appeared in The New York Times. eISBN: 9781508000877 The New York Times Company New York, NY www.nytimes.com www.nytimes.com/tbooks Obama Seeking to Boost Study of Human Brain By JOHN MARKOFF FEB. 17, 2013 The Obama administration is planning a decade-long scientific effort to examine the workings of the human brain and build a comprehensive map of its activity, seeking to do for the brain what the Human Genome Project did for genetics. The project, which the administration has been looking to unveil as early as March, will include federal agencies, private foundations and teams of neuroscientists and nanoscientists in a concerted effort to advance the knowledge of the brain’s billions of neurons and gain greater insights into perception, actions and, ultimately, consciousness. Scientists with the highest hopes for the project also see it as a way to develop the technology essential to understanding diseases like Alzheimer’sand Parkinson’s, as well as to find new therapies for a variety of mental illnesses. Moreover, the project holds the potential of paving the way for advances in artificial intelligence. The project, which could ultimately cost billions of dollars, is expected to be part of the president’s budget proposal next month. And, four scientists and representatives of research institutions said they had participated in planning for what is being called the Brain Activity Map project. -
Attempts to Achieve Immortality
Attempts to achieve immortality Attempts to achieve immortality Author: Stepanka Boudova E-mail: [email protected] Student ID: 419572 Masaryk University Brno Course: Future of Informatics 1 Attempts to achieve immortality Table of Contents Attempts to achieve immortality......................................................................................................1 Introduction......................................................................................................................................3 Historical context.............................................................................................................................3 History of Alchemy.....................................................................................................................3 Religion and Immortality............................................................................................................ 5 Symbols of Immortality.............................................................................................................. 6 The future.........................................................................................................................................6 Mind Uploading.......................................................................................................................... 6 Cryonics.................................................................................................................................... 10 2 Attempts to achieve immortality Introduction -
Application of Stochastic Modeling Techniques to Long-Term, High-Resolution Time-Lapse Microscopy Of
Quantitative analysis of axon elongation: A novel application of stochastic modeling techniques to long-term, high-resolution time-lapse microscopy of cortical axons MASSACHUSETTS INSTTilTE OF TECHNOLOGY by APR 0 1 2010 Neville Espi Sanjana LIBRARIES B.S., Symbolic Systems, Stanford University (2001) A.B., English, Stanford University (2001) Submitted to the Department of Brain & Cognitive Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Neuroscience at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2010 @ Massachusetts Institute of Technology 2010. All rights reserved. A u th or ....... ...................................... Department of Brain & Cognitive Sciences December 17, 2009 Certified by..... H. Sebastian Seung Professor of Computational Neuroscience - 40" / Thesis Supervisor Accepted by............................ Earl . Miller Earl K. Miller Picower Professor of Neuroscience Chairman, Department Committee on Graduate Theses Quantitative analysis of axon elongation: A novel application of stochastic modeling techniques to long-term, high-resolution time-lapse microscopy of cortical axons by Neville Espi Sanjana Submitted to the Department of Brain & Cognitive Sciences on December 17, 2009, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Neuroscience Abstract Axons exhibit a rich variety of behaviors, such as elongation, turning, branching, and fas- ciculation, all in service of the complex goal of wiring up the brain. In order to quantify these behaviors, I have developed a system for in vitro imaging of axon growth cones with time-lapse fluorescence microscopy. Image tiles are automatically captured and assembled into a mosaic image of a square millimeter region. GFP-expressing mouse cortical neurons can be imaged once every few minutes for up to weeks if phototoxicity is minimized. -
An Error Detection and Correction Framework for Connectomics
An Error Detection and Correction Framework for Connectomics Jonathan Zung Ignacio Tartavull∗ Kisuk Leey Princeton University Princeton University MIT [email protected] [email protected] [email protected] H. Sebastian Seung Princeton University [email protected] Abstract We define and study error detection and correction tasks that are useful for 3D reconstruction of neurons from electron microscopic imagery, and for image seg- mentation more generally. Both tasks take as input the raw image and a binary mask representing a candidate object. For the error detection task, the desired output is a map of split and merge errors in the object. For the error correction task, the desired output is the true object. We call this object mask pruning, because the candidate object mask is assumed to be a superset of the true object. We train multiscale 3D convolutional networks to perform both tasks. We find that the error-detecting net can achieve high accuracy. The accuracy of the error-correcting net is enhanced if its input object mask is “advice” (union of erroneous objects) from the error-detecting net. 1 Introduction While neuronal circuits can be reconstructed from volumetric electron microscopic imagery, the process has historically [39] and even recently [37] been highly laborious. One of the most time- consuming reconstruction tasks is the tracing of the brain’s “wires,” or neuronal branches. This task is an example of instance segmentation, and can be automated through computer detection of the boundaries between neurons. Convolutional networks were first applied to neuronal boundary detection a decade ago [14, 38]. Since then convolutional nets have become the standard approach, arXiv:1708.02599v2 [cs.CV] 3 Dec 2017 and the accuracy of boundary detection has become impressively high [40, 3, 21, 9]. -
Seymour Knowles-Barley1*, Mike Roberts2, Narayanan Kasthuri1, Dongil Lee1, Hanspeter Pfister2 and Jeff W. Lichtman1
Mojo 2.0: Connectome Annotation Tool Seymour Knowles-Barley1*, Mike Roberts2, Narayanan Kasthuri1, Dongil Lee1, Hanspeter Pfister2 and Jeff W. Lichtman1 1Harvard University, Department of Molecular and Cellular Biology, USA 2Harvard University, School of Engineering and Applied Sciences, USA Available online at http://www.rhoana.org/ Introduction A connectome is the wiring diagram of connections in a nervous system. Mapping this network of connections is necessary for discovering the underlying architecture of the brain and investigating the physical underpinning of cognition, intelligence, and consciousness [1, 2, 3]. It is also an important step in understanding how connectivity patterns are altered by mental illnesses, learning disorders, and age related changes in the brain. Fully automatic computer vision techniques are available to segment electron microscopy (EM) connectome data. Currently we use the Rhoana pipeline to process images, but results are still far from perfect and require additional human annotation to produce an accurate connectivity map [4]. Here we present Mojo 2.0, an open source, interactive, scalable annotation tool to correct errors in automatic segmentation results. Mojo Features ● Interactive annotation tool ● Smart scribble interface for split and merge operations ● Scalable up to TB scale volumes ● Entire segmentation pipeline including The Mojo interface displaying an EM section from mouse cortex, approximately 7.5x5μm. Mojo is open source and available online: http://www.rhoana.org/ Annotation Results A mouse brain cortex dataset was annotated with the Rhoana image processing pipeline and corrected using Mojo. Partial annotations over two sub volumes totaling 2123μm3 were made by novice users. On average, 1μm3 was annotated in 15 minutes and required 126 edit operations. -
Reverse-Engineering the Brain, Intersymp-2016, 1 August, 2016 RESU109-IS16
Jens Pohl: Reverse-Engineering the Brain, InterSymp-2016, 1 August, 2016 RESU109-IS16 Reverse-Engineering the Brain: The parts are as complex as the whole. Jens Pohl, PhD Professor of Architecture (Emeritus) California Polytechnic State University (Cal Poly) Vice President, Engineering & Technology Tapestry Solutions (a Boeing Company) San Luis Obispo, California, USA Abstract The purpose of this paper is to review the current state of neuroscience research with a focus on what has been achieved to date in unraveling the mysteries of brain operations, major research initiatives, fundamental challenges, and potentially realizable objectives. General research approaches aimed at constructing a wiring diagram of the brain (i.e., connectome), determining how the brain encodes and computes information, and whole brain simulation attempts are reviewed in terms of strategies employed and difficulties encountered. While promising advances have been made during the past 50 years due to electron microscopy, the development of new experimental methods, and the availability of computer-enabled high throughput imaging systems, brain research is still greatly encumbered by inadequate monitoring and recording capabilities. Four hypotheses relating to comprehension through the assembly of parts, formation of memories, influence of genes, and synapse formation are described as plausible explanations even though they cannot be validated at this time. By assessing the feasibility of overcoming the principal problems that beleaguer brain research in comparison -
Publications of Sebastian Seung
Publications of Sebastian Seung V. Jain, H. S. Seung, and S. C. Turaga. Machines that learn to segment images: a crucial technology for connectomics. (pdf) Curr. Opin. Neurobiol (2010). V. Jain, B. Bollmann, M. Richardson, D. Berger et al. Boundary learning by optimization with topological constraints. (pdf supplementary info) Proceedings of the IEEE 23rd Conference on Computer Vision and Pattern Recognition (2010). I. R. Wickersham, H. A. Sullivan, and H. S. Seung. Production of glycoprotein-deleted rabies viruses for monosynaptic tracing and high-level gene expression in neurons. (pdf) Nature Protocols. 5, 596-606 (2010). S. C. Turaga, J. F. Murray, V. Jain, F. Roth, M. Helmstaedter, K. Briggman, W. Denk, and H. S. Seung. Convolutional networks can learn to generate affinity graphs for image segmentation. (pdf) Neural Computation 22, 511-538 (2010). S. C. Turaga, K. L. Briggman, M. Helmstaedter, W. Denk, and H. S. Seung. Maximin affinity learning of image segmentation. (pdf) Adv. Neural Info. Proc. Syst. 22 (Proceedings of NIPS '09) (2010). J. Wang, M. T. Hasan, and H. S. Seung. Laser-evoked synaptic transmission in cultured hippocampal neurons expressing Channelrhodopsin-2 delivered by adeno-associated virus.(pdf) J. Neurosci. Methods 183, 165-175 (2009). Y. Loewenstein, D. Prelec, and H. S. Seung. Operant matching as a Nash equilibrium of an intertemporal game. (pdf) Neural Computation 21, 2755-2773 (2009). H. S. Seung. Reading the Book of Memory: Sparse Sampling versus Dense Mapping of Connectomes. (pdf) Neuron 62, 17-29 (2009). V. Jain and H. S. Seung. Natural Image Denoising with Convolutional Networks (pdf) Adv. Neural Info. -
The Ethics of Exponential Life Extension Through Brain Preservation
A peer-reviewed electronic journal published by the Institute for Ethics and Emerging Technologies ISSN 1541-0099 26(1) – March 2016 The Ethics of Exponential Life Extension through Brain Preservation Michael A. Cerullo Brain Preservation Foundation [email protected] Journal of Evolution and Technology - Vol. 26 Issue 1 – March 2016 - pgs 94-105 Abstract Chemical brain preservation allows the brain to be preserved for millennia. In the coming decades, the information in a chemically preserved brain may be able to be decoded and emulated in a computer. I first examine the history of brain preservation and recent advances that indicate this may soon be a real possibility. I then argue that chemical brain preservation should be viewed as a life-saving medical procedure. Any technology that significantly extends the human life span faces many potential criticisms. However, standard medical ethics entails that individuals should have the autonomy to choose chemical brain preservation. Only if the harm to society caused by brain preservation and future emulation greatly outweighed any potential benefit would it be ethically acceptable to refuse individuals this medical intervention. Since no such harm exists, it is ethical for individuals to choose chemical brain preservation. Introduction One essential part of the definition of life is the drive to preserve existence. Thus it is not surprising that life extension has been a key concern of humanity throughout recorded history (Cave 2012). In the recent past, extending the human life span beyond the “natural” limit was seen as selfish, dangerous, and immoral (Fukuyama 2002; Kass 2003; President’s Council on Bioethics 2003; Pijnenburg and Leget 2006; Blow 2013). -
Connectome Seymour Knowles-Barley1,2, Thouis R
CATEGORY: COMPUTER VISION - CV04 POSTER CONTACT NAME P4212 Thouis Jones: [email protected] Deep Learning for the Connectome Seymour Knowles-Barley1,2, Thouis R. Jones1,2, Josh Morgan2, Dongil Lee2, Narayanan Kasthuri2, Jeff W. Lichtman2 and Hanspeter Pfister1. 1Harvard University, School of Engineering and Applied Sciences, USA. 2Harvard University, Department of Molecular and Cellular Biology, USA. Introduction The connectome is the wiring diagram of the nervous system. Mapping this network is necessary for discovering the underlying architecture of the brain and investigating the physical underpinning of cognition, intelligence, and consciousness [1, 2, 3]. It is also an important step in understanding how connectivity patterns are altered by mental illnesses, learning disorders, and age related changes in the brain. In our experiments, brain tissue is microtomed and imaged with an electron microscope at a resolution of 4x4x30nm per voxel. Cell membranes and sub-cellular structures such as mitochondria, synapses and vesicles are clearly visible in the images. We have developed a fully automatic segmentation pipelines [2] to annotate this data. The requirement of a very high level of accuracy and data rates approaching 1TB a day make this a challenging computer vision task. Deep Learning The first and most critical stage of the our segmentation pipeline (www.rhoana.org) is to identify membrane in the 2D images produced by the microscope. We use convolutional neural networks to classify images, trained on a few hand-annotated images. GPU computing greatly reduces the training and classification time for these computationally demanding networks. In this work, we used pylearn2 [5] to apply maxout networks [6] to two connectome data sets.