Sensorimotor Integration an a Small Motor Circuit
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Designing Biological Systems
Downloaded from genesdev.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press REVIEW Designing biological systems David A. Drubin,1 Jeffrey C. Way,2 and Pamela A. Silver1,3 1Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA; 2EMD Lexigen Research Center, Billerica, Massachusetts 01821, USA The design of artificial biological systems and the under- chemical systems that mimic living systems, especially standing of their natural counterparts are key objectives in regard to replication and Darwinian selection. This of the emerging discipline of synthetic biology. Toward review will primarily focus on accomplishments of the both ends, research in synthetic biology has primarily first strategy; however, readers are referred to several fine focused on the construction of simple devices, such as reviews on the generation of artificial, life-emulating transcription-based oscillators and switches. Construc- systems (Szostak et al. 2001; Benner 2003; Benner and tion of such devices should provide us with insight on Sismour 2005). the design of natural systems, indicating whether our According to the synthetic biology paradigm, a syn- understanding is complete or whether there are still gaps thetically programmed cell should be composed of many in our knowledge. Construction of simple biological sys- subsystems that operate successfully as a result of ex- tems may also lay the groundwork for the construction tensive characterization and educated design. System of more complex systems that have practical utility. To construction is the product of iterative cycles of com- realize its full potential, biological systems design bor- puter modeling, biological assembly, and testing. In this rows from the allied fields of protein design and meta- way, much from the field of systems biology can be ap- bolic engineering. -
Are Systems Neuroscience Explanations Mechanistic?
Are Systems Neuroscience Explanations Mechanistic? Carlos Zednik [email protected] Institute of Cognitive Science, University of Osnabrück 49069 Osnabrück, Germany Paper to be presented at: Philosophy of Science Association 24th Biennial Meeting (Chicago, IL), November 2014 Abstract Whereas most branches of neuroscience are thought to provide mechanistic explanations, systems neuroscience is not. Two reasons are traditionally cited in support of this conclusion. First, systems neuroscientists rarely, if ever, rely on the dual strategies of decomposition and localization. Second, they typically emphasize organizational properties over the properties of individual components. In this paper, I argue that neither reason is conclusive: researchers might rely on alternative strategies for mechanism discovery, and focusing on organization is often appropriate and consistent with the norms of mechanistic explanation. Thus, many explanations in systems neuroscience can also be viewed as mechanistic explanations. 1 1. Introduction There is a widespread consensus in philosophy of science that neuroscientists provide mechanistic explanations. That is, they seek the discovery and description of the mechanisms responsible for the behavioral and neurological phenomena being explained. This consensus is supported by a growing philosophical literature on past and present examples from various branches of neuroscience, including molecular (Craver 2007; Machamer, Darden, and Craver 2000), cognitive (Bechtel 2008; Kaplan and Craver 2011), and computational -
Systems Neuroscience of Mathematical Cognition and Learning
CHAPTER 15 Systems Neuroscience of Mathematical Cognition and Learning: Basic Organization and Neural Sources of Heterogeneity in Typical and Atypical Development Teresa Iuculano, Aarthi Padmanabhan, Vinod Menon Stanford University, Stanford, CA, United States OUTLINE Introduction 288 Ventral and Dorsal Visual Streams: Neural Building Blocks of Mathematical Cognition 292 Basic Organization 292 Heterogeneity in Typical and Atypical Development 295 Parietal-Frontal Systems: Short-Term and Working Memory 296 Basic Organization 296 Heterogeneity in Typical and Atypical Development 299 Lateral Frontotemporal Cortices: Language-Mediated Systems 302 Basic Organization 302 Heterogeneity in Typical and Atypical Development 302 Heterogeneity of Function in Numerical Cognition 287 https://doi.org/10.1016/B978-0-12-811529-9.00015-7 © 2018 Elsevier Inc. All rights reserved. 288 15. SYSTEMS NEUROSCIENCE OF MATHEMATICAL COGNITION AND LEARNING The Medial Temporal Lobe: Declarative Memory 306 Basic Organization 306 Heterogeneity in Typical and Atypical Development 306 The Circuit View: Attention and Control Processes and Dynamic Circuits Orchestrating Mathematical Learning 310 Basic Organization 310 Heterogeneity in Typical and Atypical Development 312 Plasticity in Multiple Brain Systems: Relation to Learning 314 Basic Organization 314 Heterogeneity in Typical and Atypical Development 315 Conclusions and Future Directions 320 References 324 INTRODUCTION Mathematical skill acquisition is hierarchical in nature, and each iteration of increased proficiency builds on knowledge of a lower-level primitive. For example, learning to solve arithmetical operations such as “3 + 4” requires first an understanding of what numbers mean and rep- resent (e.g., the symbol “3” refers to the quantity of three items, which derives from the ability to attend to discrete items in the environment). -
A Meta-Analysis of Functional MRI Results
RESEARCH Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results 1 1 1 2 Katherine L. Bottenhorn , Jessica S. Flannery , Emily R. Boeving , Michael C. Riedel , 3,4 1 2 Simon B. Eickhoff , Matthew T. Sutherland , and Angela R. Laird 1Department of Psychology, Florida International University, Miami, FL, USA 2Department of Physics, Florida International University, Miami, FL, USA 3Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany Downloaded from http://direct.mit.edu/netn/article-pdf/3/1/27/1092290/netn_a_00050.pdf by guest on 01 October 2021 4Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany an open access journal Keywords: Neuroimaging meta-analysis, Naturalistic paradigms, Clustering analysis, Neuro- informatics ABSTRACT Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use Citation: Bottenhorn, K. L., Flannery, highly controlled tasks to isolate mental processes of interest. However, a growing literature J. S., Boeving, E. R., Riedel, M. C., Eickhoff, S. B., Sutherland, M. T., & shows how dynamic, interactive tasks have allowed researchers to study cognition as it more Laird, A. R. (2019). Cooperating yet naturally occurs. Collective analysis across such neuroimaging experiments may answer distinct brain networks engaged during naturalistic paradigms: broader questions regarding how naturalistic cognition is biologically distributed throughout A meta-analysis of functional MRI k results. Network Neuroscience, the brain. We applied an unbiased, data-driven, meta-analytic approach that uses -means 3(1), 27–48. -
The NEURONS and NEURAL SYSTEM: a 21St CENTURY PARADIGM
The NEURONS and NEURAL SYSTEM: a 21st CENTURY PARADIGM This material is excerpted from the full β-version of the text. The final printed version will be more concise due to further editing and economical constraints. A Table of Contents and an index are located at the end of this paper. A few citations have yet to be defined and are indicated by “xxx.” James T. Fulton Neural Concepts [email protected] July 19, 2015 Copyright 2011 James T. Fulton 2 Neurons & the Nervous System 4 The Architectures of Neural Systems1 [xxx review cogn computation paper and incorporate into this chapter ] [xxx expand section 4.4.4 as a key area of importance ] [xxx Text and semantics needs a lot of work ] Don’t believe everything you think. Anonymous bumper sticker You must not fool yourself, and you are the easiest person to fool Richard Feynman I am never content until I have constructed a model of what I am studying. If I succeed in making one, I understand; otherwise, I do not. William Thomson (Lord Kelvin) It is the models that tell us whether we understand a process and where the uncertainties remain. Bridgeman, 2000 4.1 Background [xxx chapter is a hodge-podge at this time 29 Aug 11 ] The animal kingdom shares a common neurological architecture that is ramified in a specific species in accordance with its station in the phylogenic tree and the ecological domain. This ramification includes not only replication of existing features but further augmentation of the system using new and/or modified features. -
Systems Processes and Pathologies: Creating an Integrated Framework for Systems Science
IS13-SysProc&Path-Troncale.docx Page 1 of 24 Systems Processes and Pathologies: Creating An Integrated Framework for Systems Science Dr. Len Troncale Professor Emeritus and Past Chair, Biological Sciences Director, Institute for Advanced Systems Science California State Polytechnic University, Pomona, California, 91711 [email protected] Copyright © 2013 by Len Troncale. Published and used by INCOSE with permission Abstract. Among the several official projects of the INCOSE Systems Science Working Group, one focuses on integrating the plethora of systems theories, sources, approaches, and tools developed over the past half-century with the purpose of enabling a new and unified “science” of systems as a fundamental basis for SE. Another seeks to develop a much more SE-usable Systems Pathology also grounded in a “science” of systems. This paper introduces the wider SE community to the current status of this unique knowledge base produced over the past three years by an INCOSE-ISSS alliance summarizing the current output of 7 Workshops, 12 Papers, >24 Presentations or Webinars, and 5 Reports. It describes the need for integration of systems knowledge by demonstrating the extensive fragmentation of numerous contributing fields. It presents the current 12-step “protocol” used by the current group to guide its efforts at synthesis across systems domains, disciplines, tools, and scales asking for feedback to improve the approach. It introduces 15 Working Assumptions or Hypotheses that form the foundation for this attempt at unification citing why these could be used as working principles but why it may be undesirable to call them “principles” as others often do. The paper presents working frameworks for integration and criteria used to judge whether results are a “science” of systems or not with reminders that these early guidelines are being subjected to constant testing and revision. -
The Device Approach to Emergent Properties
arXiv:1801.05452, Version 3 Asking Biological Questions of Physical Systems: the Device Approach to Emergent Properties Bob Eisenberg Department of Applied Mathematics Illinois Institute of Technology USA Department of Physiology and Biophysics Rush University USA [email protected] January 17, 2018 9/23/2021 9:14 AM Abstract Life occurs in concentrated ‘Ringer Solutions’ derived from seawater that Lesser Blum studied for most of his life. As we worked together, Lesser and I realized that the questions asked of those solutions were quite different in biology from those in the physical chemistry he knew. Biology is inherited. Information is passed by handfuls of atoms in the genetic code. A few atoms in the proteins built from the code change macroscopic function. Indeed, a few atoms often control biological function in the same sense that a gas pedal controls the speed of a car. Biological questions then are most productive when they are asked in the context of evolution. What function does a system perform? How is the system built to perform that function? What forces are used to perform that function? How are the modules that perform functions connected to make the machinery of life. Physiologists have shown that much of life is a nested hierarchy of devices, one on top of another, linking atomic ions in concentrated solutions to current flow through proteins, current flow to voltage signals, voltage signals to changes in current flow, all connected to make a regenerative system that allows electrical action potentials to move meters, under the control of a few atoms. -
SYSTEMS NEUROSCIENCE (FALL 2018) [Neurobio 208 and Anat 210]
SYSTEMS NEUROSCIENCE (FALL 2018) [Neurobio 208 and Anat 210] Neurbio 208 is required for 1st year graduate students in Neurobiology and Behavior and serves as “S” area core courses for the INP. Anat 210 is open to all graduate students in Anatomy and Neurobiology. Graduate students from other departments may enroll in either Neurbio 208 or Anat 210 with permission from the course director, Dr. Ron Frostig. Time/place: 9:00-10:20AM, MWF in MH 2246 Text: Neuroscience, 6th Edition, Purves, D. et al. (Eds.) Sinauer, 2017. The instructors will distribute other readings. Exams and grading: The final grade will be based on performance on the midterm exams. The instructor for that section will announce the format of each exam. Exams will be predominately essay. There will be no cumulative final exam, and grades will be normalized to the number of lectures leading to the midterm. Final grade will be based on averaging of all midterms. Participating Faculty: Neurobiology & Behavior Anatomy & Neurobiology Ron Frostig, director Steve Cramer Steve Mahler Fall 2018 Date Topic Instructor Readings (in text unless noted) Fri Principles of Mahler https://my.rocketmix.com/enrollcourse.aspx?courseid=3087 9/28 Brain --Please enroll in this and look at it BEFORE CLASS Organization Other helpful resources: http://zoomablebrain.bio.uci.edu/ http://www.exploratorium.edu/memory/braindissection/ Mon Neuroanatomy- Mahler William James, 1890, chapter 2 (http://psychclassics.yorku.ca/ 10/1 Dissection James/Principles/prin2.htm) Wed Introduction to Frostig 10/3 sensory systems Fri The eye: Frostig Ch.11 10/5 structure and function Mon The eye: Frostig Ch.11 10/8 structure and function Wed Central visual Frostig Ch.12 10/10 pathways I Fri Central visual Frostig Ch. -
Systems Biology and Its Relevance to Alcohol Research
Commentary: Systems Biology and Its Relevance to Alcohol Research Q. Max Guo, Ph.D., and Sam Zakhari, Ph.D. Systems biology, a new scientific discipline, aims to study the behavior of a biological organization or process in order to understand the function of a dynamic system. This commentary will put into perspective topics discussed in this issue of Alcohol Research & Health, provide insight into why alcohol-induced disorders exemplify the kinds of conditions for which a systems biological approach would be fruitful, and discuss the opportunities and challenges facing alcohol researchers. KEY WORDS: Alcohol-induced disorders; alcohol research; biomedical research; systems biology; biological systems; mathematical modeling; genomics; epigenomics; transcriptomics; metabolomics; proteomics ntil recently, most biologists’ emerging discipline that deals with, Alcohol Research & Health intend to efforts have been devoted to and takes advantage of, these enormous address. In this commentary, we will Ureducing complex biological amounts of data. Although scientists try to put the topics discussed in this systems to the properties of individual and engineers have applied the concept issue into perspective, provide views molecules. However, with the com of an integrated systemic approach for on the significance of systems biology pleted sequencing of the genomes of years, systems biology has only emerged as a new, distinct discipline to study 1 High-throughput genomics is the study of the structure humans, mice, rats, and many other and function of an organism’s complete genetic content, organisms, technological advances in complex biological systems in the past or genome, using technology that analyzes a large num the fields of high-throughput genomics1 several years. -
Communication Theory in Biological Systems
SUMMARY CDI TYPE I: A Communications Theory Approach to Morphogenesis and Architecture Maintenance The assertion that biological systems are communication networks would draw no rebuke from biologists – the terms signaling, communication, and network are deeply embedded parts of the biology parlance. However, the more profound meanings of information and communication are often overlooked when considering biological systems. Information can be quantified, its flow can be measured and tight bounds exist for its representation and conveyance between transmitters and receivers in a variety of settings. Furthermore, communications theory is about efficient com- munication where energy is at a premium – as is often the case in organisms. But perhaps most important, information theory allows mechanism-blind bounds on decisions and information flow. That is, the physics of a system allows determination of limits that any method of information description, delivery or processing must obey. Thus, rigorous application of communication theory to complex multi-cellular biological sys- tems seems both attractive and obvious as an organizing principle – a way to tease order from the myriad engineering solutions that comprise biological systems. Likewise, study of biological systems – engineering solutions evolved over eons – might yield new communication and com- putation theory. Yet so far, a communications-theoretic approach to multi-cellular biology has received scant, if any, attention. We therefore propose to explore this interdisciplinary intellectual -
Systems Neuroscience of Natural Behaviors in Rodents
The Journal of Neuroscience, February 3, 2021 • 41(5):911–919 • 911 Symposium/Mini-Symposium Systems Neuroscience of Natural Behaviors in Rodents Emily Jane Dennis,1 Ahmed El Hady,1 Angie Michaiel,2 Ann Clemens,3 Dougal R. Gowan Tervo,4 Jakob Voigts,5 and Sandeep Robert Datta6 1Princeton University and Howard Hughes Medical Institute, Princeton, New Jersey, 08540, 2University of Oregon, Eugene, Oregon, 97403-1254, 3University of Edinburgh, Edinburgh, Scotland, EH8 9JZ, 4Janelia Research Campus, Ashburn, Virginia, 20147, 5Massachusetts Institute of Technology, Cambridge, Massachusets, 02139, and 6Harvard University, Cambridge, Massachusets, 02115 Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments. Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely moving animals in more natural conditions while applying systems techniques: performing temporally specific perturbations, modeling behavioral strategies, and record- ing from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. -
Systems Neuroscience of Natural Behaviors in Rodents
Systems Neuroscience of Natural Behaviors in Rodents 1,2 1,2 3 4 Emily Jane Dennis* , Ahmed El Hady , Angie Michaiel , Ann Clemens , Dougal R. Gowan 5 6 3 Tervo , Jakob Voigts , and Sandeep Robert Datta Abstract Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments.Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely-moving animals in more natural conditions while applying systems techniques: performing temporally-specific perturbations, modeling behavioral strategies, and recording from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. Here we discuss the origins of ethology, ecology, and systems neuroscience in the context of our own work and highlight how combining approaches across these fields has provided fresh insights into our research. We hope this review facilitates some of these interactions and alliances and helps us all do even better science, together.