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An Investigation of the Cortical Learning Algorithm
Rowan University Rowan Digital Works Theses and Dissertations 5-24-2018 An investigation of the cortical learning algorithm Anthony C. Samaritano Rowan University Follow this and additional works at: https://rdw.rowan.edu/etd Part of the Electrical and Computer Engineering Commons, and the Neuroscience and Neurobiology Commons Recommended Citation Samaritano, Anthony C., "An investigation of the cortical learning algorithm" (2018). Theses and Dissertations. 2572. https://rdw.rowan.edu/etd/2572 This Thesis is brought to you for free and open access by Rowan Digital Works. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Rowan Digital Works. For more information, please contact [email protected]. AN INVESTIGATION OF THE CORTICAL LEARNING ALGORITHM by Anthony C. Samaritano A Thesis Submitted to the Department of Electrical and Computer Engineering College of Engineering In partial fulfillment of the requirement For the degree of Master of Science in Electrical and Computer Engineering at Rowan University November 2, 2016 Thesis Advisor: Robi Polikar, Ph.D. © 2018 Anthony C. Samaritano Acknowledgments I would like to express my sincerest gratitude and appreciation to Dr. Robi Polikar for his help, instruction, and patience throughout the development of this thesis and my research into cortical learning algorithms. Dr. Polikar took a risk by allowing me to follow my passion for neurobiologically inspired algorithms and explore this emerging category of cortical learning algorithms in the machine learning field. The skills and knowledge I acquired throughout my research have definitively molded me into a more diligent and thorough engineer. I have, and will continue to, take these characteristics and skills I have gained into my current and future professional endeavors. -
Many Specialists for Suppressing Cortical Excitation Andreas Burkhalter Washington University School of Medicine in St
Washington University School of Medicine Digital Commons@Becker Open Access Publications 12-2008 Many specialists for suppressing cortical excitation Andreas Burkhalter Washington University School of Medicine in St. Louis Follow this and additional works at: https://digitalcommons.wustl.edu/open_access_pubs Part of the Medicine and Health Sciences Commons Recommended Citation Burkhalter, Andreas, ,"Many specialists for suppressing cortical excitation." Frontiers in Neuroscience.,. 1-167. (2008). https://digitalcommons.wustl.edu/open_access_pubs/495 This Open Access Publication is brought to you for free and open access by Digital Commons@Becker. It has been accepted for inclusion in Open Access Publications by an authorized administrator of Digital Commons@Becker. For more information, please contact [email protected]. FOCUSED REVIEW published: 15 December 2008 doi: 10.3389/neuro.01.026.2008 Many specialists for suppressing cortical excitation Andreas Burkhalter* Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO, USA Cortical computations are critically dependent on GABA-releasing neurons for dynamically balancing excitation with inhibition that is proportional to the overall level of activity. Although it is widely accepted that there are multiple types of interneurons, defining their identities based on qualitative descriptions of morphological, molecular and physiological features has failed to produce a universally accepted ‘parts list’, which is needed to understand the roles that interneurons play in cortical processing. A list of features has been published by the Petilla Interneurons Nomenclature Group, which represents an important step toward an unbiased classification of interneurons. To this end some essential features have recently been studied quantitatively and their association was examined using multidimensional cluster analyses. -
Cortical Layers: What Are They Good For? Neocortex
Cortical Layers: What are they good for? Neocortex L1 L2 L3 network input L4 computations L5 L6 Brodmann Map of Cortical Areas lateral medial 44 areas, numbered in order of samples taken from monkey brain Brodmann, 1908. Primary visual cortex lamination across species Balaram & Kaas 2014 Front Neuroanat Cortical lamination: not always a six-layered structure (archicortex) e.g. Piriform, entorhinal Larriva-Sahd 2010 Front Neuroanat Other layered structures in the brain Cerebellum Retina Complexity of connectivity in a cortical column • Paired-recordings and anatomical reconstructions generate statistics of cortical connectivity Lefort et al. 2009 Information flow in neocortical microcircuits Simplified version “computational Layer 2/3 layer” Layer 4 main output Layer 5 main input Layer 6 Thalamus e - excitatory, i - inhibitory Grillner et al TINS 2005 The canonical cortical circuit MAYBE …. DaCosta & Martin, 2010 Excitatory cell types across layers (rat S1 cortex) Canonical models do not capture the diversity of excitatory cell classes Oberlaender et al., Cereb Cortex. Oct 2012; 22(10): 2375–2391. Coding strategies of different cortical layers Sakata & Harris, Neuron 2010 Canonical models do not capture the diversity of firing rates and selectivities Why is the cortex layered? Do different layers have distinct functions? Is this the right question? Alternative view: • When thinking about layers, we should really be thinking about cell classes • A cells class may be defined by its input connectome and output projectome (and some other properties) • The job of different classes is to (i) make associations between different types of information available in each cortical column and/or (ii) route/gate different streams of information • Layers are convenient way of organising inputs and outputs of distinct cell classes Excitatory cell types across layers (rat S1 cortex) INTRATELENCEPHALIC (IT) | PYRAMIDAL TRACT (PT) | CORTICOTHALAMIC (CT) From Cereb Cortex. -
Oup Cercor Bhx026 3790..3805 ++
Cerebral Cortex, July 2017;27: 3790–3805 doi: 10.1093/cercor/bhx026 Advance Access Publication Date: 10 February 2017 Original Article ORIGINAL ARTICLE Body Topography Parcellates Human Sensory and Motor Cortex Esther Kuehn1,2,3,4, Juliane Dinse5,6,†, Estrid Jakobsen7, Xiangyu Long1, Andreas Schäfer5, Pierre-Louis Bazin1,5, Arno Villringer1, Martin I. Sereno2 and Daniel S. Margulies7 1Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany, 2Department of Psychology and Language Sciences, University College London, London WC1H 0DG, UK, 3Center for Behavioral Brain Sciences Magdeburg, Magdeburg 39106, Germany, 4Aging and Cognition Research Group, DZNE, Magdeburg 39106, Germany, 5Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany, 6Faculty of Computer Science, Otto-von- Guericke University, Magdeburg 39106, Germany and 7Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany Address correspondence to Esther Kuehn, Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany. Email: [email protected] † Co-first author. Abstract The cytoarchitectonic map as proposed by Brodmann currently dominates models of human sensorimotor cortical structure, function, and plasticity. According to this model, primary motor cortex, area 4, and primary somatosensory cortex, area 3b, are homogenous areas, with the major division lying between the two. Accumulating empirical and theoretical evidence, however, has begun to question the validity of the Brodmann map for various cortical areas. Here, we combined in vivo cortical myelin mapping with functional connectivity analyses and topographic mapping techniques to reassess the validity of the Brodmann map in human primary sensorimotor cortex. -
New Map of Brain's Surface Unites
Spectrum | Autism Research News https://www.spectrumnews.org NEWS New map of brain’s surface unites structure, function BY NICHOLETTE ZELIADT 29 AUGUST 2016 Researchers have charted the human cerebral cortex in unprecedented detail, adding to what is known about the brain’s bumpy outer layer. The map could serve as a reference to help researchers identify alterations in the brains of people with autism1. The cortex has distinct regions with specialized functions that are not obvious to the naked eye, but microscopes and brain scans can reveal variations in its structure and function. Existing maps of the cortex are typically based on a single anatomical or functional feature. The new map, described 20 July in Nature, combines four measures: cortical thickness, the amount of insulation around nerves and two types of neural activity patterns. “We’ve got reasonably accurate descriptions of nearly all of the major cortical areas,” says lead investigator David Van Essen, alumni endowed professor of neuroscience at Washington University in St. Louis, Missouri. The map plots previously unmapped regions of the cortex and divides established ones into parts, giving researchers an expanded view of the brain. “There’s a lot more going on and a lot more detail there than we ever imagined,” says Kevin Pelphrey, director of the Autism and Neurodevelopmental Disorders Institute at George Washington University in Washington, D.C., who was not involved in the work. Cortical compartments: German neuroanatomist Korbinian Brodmann drew one of the first maps of the cerebral cortex by hand in 1909 after examining postmortem brains under a microscope. -
Scaling the Htm Spatial Pooler
International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.4, July 2020 SCALING THE HTM SPATIAL POOLER 1 2 1 1 Damir Dobric , Andreas Pech , Bogdan Ghita and Thomas Wennekers 1University of Plymouth, Faculty of Science and Engineering, UK 2Frankfurt University of Applied Sciences, Dept. of Computer Science and Engineering, Germany ABSTRACT The Hierarchical Temporal Memory Cortical Learning Algorithm (HTM CLA) is a theory and machine learning technology that aims to capture cortical algorithm of the neocortex. Inspired by the biological functioning of the neocortex, it provides a theoretical framework, which helps to better understand how the cortical algorithm inside of the brain might work. It organizes populations of neurons in column-like units, crossing several layers such that the units are connected into structures called regions (areas). Areas and columns are hierarchically organized and can further be connected into more complex networks, which implement higher cognitive capabilities like invariant representations. Columns inside of layers are specialized on learning of spatial patterns and sequences. This work targets specifically spatial pattern learning algorithm called Spatial Pooler. A complex topology and high number of neurons used in this algorithm, require more computing power than even a single machine with multiple cores or a GPUs could provide. This work aims to improve the HTM CLA Spatial Pooler by enabling it to run in the distributed environment on multiple physical machines by using the Actor Programming Model. The proposed model is based on a mathematical theory and computation model, which targets massive concurrency. Using this model drives different reasoning about concurrent execution and enables flexible distribution of parallel cortical computation logic across multiple physical nodes. -
Universal Transition from Unstructured to Structured Neural Maps
Universal transition from unstructured to structured PNAS PLUS neural maps Marvin Weiganda,b,1, Fabio Sartoria,b,c, and Hermann Cuntza,b,d,1 aErnst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt/Main D-60528, Germany; bFrankfurt Institute for Advanced Studies, Frankfurt/Main D-60438, Germany; cMax Planck Institute for Brain Research, Frankfurt/Main D-60438, Germany; and dFaculty of Biological Sciences, Goethe University, Frankfurt/Main D-60438, Germany Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved April 5, 2017 (received for review September 28, 2016) Neurons sharing similar features are often selectively connected contradictory to experimental observations (24, 25). Structured with a higher probability and should be located in close vicinity to maps in the visual cortex have been described in primates, carni- save wiring. Selective connectivity has, therefore, been proposed vores, and ungulates but are reported to be absent in rodents, to be the cause for spatial organization in cortical maps. In- which instead exhibit unstructured, seemingly random arrange- terestingly, orientation preference (OP) maps in the visual cortex ments commonly referred to as salt-and-pepper configurations are found in carnivores, ungulates, and primates but are not found (26–28). Phase transitions between an unstructured and a struc- in rodents, indicating fundamental differences in selective connec- tured map have been described in a variety of models as a function tivity that seem unexpected for closely related species. Here, we of various model parameters (12, 13). Still, the biological correlate investigate this finding by using multidimensional scaling to of the phase transition and therefore, the reason for the existence of predict the locations of neurons based on minimizing wiring costs structured and unstructured neural maps in closely related species for any given connectivity. -
Cortical Columns: Building Blocks for Intelligent Systems
Cortical Columns: Building Blocks for Intelligent Systems Atif G. Hashmi and Mikko H. Lipasti Department of Electrical and Computer Engineering University of Wisconsin - Madison Email: [email protected], [email protected] Abstract— The neocortex appears to be a very efficient, uni- to be completely understood. A neuron, the basic structural formly structured, and hierarchical computational system [25], unit of the neocortex, is orders of magnitude slower than [23], [24]. Researchers have made significant efforts to model a transistor, the basic structural unit of modern computing intelligent systems that mimic these neocortical properties to perform a broad variety of pattern recognition and learning systems. The average firing interval for a neuron is around tasks. Unfortunately, many of these systems have drifted away 150ms to 200ms [21] while transistors can operate in less from their cortical origins and incorporate or rely on attributes than a nanosecond. Still, the neocortex performs much better and algorithms that are not biologically plausible. In contrast, on pattern recognition and other learning based tasks than this paper describes a model for an intelligent system that contemporary high speed computers. One of the main reasons is motivated by the properties of cortical columns, which can be viewed as the basic functional unit of the neocortex for this seemingly unusual behavior is that certain properties [35], [16]. Our model extends predictability minimization [30] of the neocortex–like independent feature detection, atten- to mimic the behavior of cortical columns and incorporates tion, feedback, prediction, and training data independence– neocortical properties such as hierarchy, structural uniformity, make it a quite flexible and powerful parallel processing and plasticity, and enables adaptive, hierarchical independent system. -
2 the Cerebral Cortex of Mammals
Abstract This thesis presents an abstract model of the mammalian neocortex. The model was constructed by taking a top-down view on the cortex, where it is assumed that cortex to a first approximation works as a system with attractor dynamics. The model deals with the processing of static inputs from the perspectives of biological mapping, algorithmic, and physical implementation, but it does not consider the temporal aspects of these inputs. The purpose of the model is twofold: Firstly, it is an abstract model of the cortex and as such it can be used to evaluate hypotheses about cortical function and structure. Secondly, it forms the basis of a general information processing system that may be implemented in computers. The characteristics of this model are studied both analytically and by simulation experiments, and we also discuss its parallel implementation on cluster computers as well as in digital hardware. The basic design of the model is based on a thorough literature study of the mammalian cortex’s anatomy and physiology. We review both the layered and columnar structure of cortex and also the long- and short-range connectivity between neurons. Characteristics of cortex that defines its computational complexity such as the time-scales of cellular processes that transport ions in and out of neurons and give rise to electric signals are also investigated. In particular we study the size of cortex in terms of neuron and synapse numbers in five mammals; mouse, rat, cat, macaque, and human. The cortical model is implemented with a connectionist type of network where the functional units correspond to cortical minicolumns and these are in turn grouped into hypercolumn modules. -
Toward a Mathematics of Brain Function
PERSPECTIVE Columnar connectome: toward a mathematics of brain function Anna Wang Roe Institute of Interdisciplinary Neuroscience and Technology, Zhejiang University, Hangzhou, China Keywords: Primate, Cerebral cortex, Functional networks, Functional tract tracing, Matrix mapping, Brain theory, Artificial intelligence Downloaded from http://direct.mit.edu/netn/article-pdf/3/3/779/1092449/netn_a_00088.pdf by guest on 01 October 2021 ABSTRACT an open access journal Understanding brain networks is important for many fields, including neuroscience, psychology, medicine, and artificial intelligence. To address this fundamental need, there are multiple ongoing connectome projects in the United States, Europe, and Asia producing brain connection maps with resolutions at macro- and microscales. However, still lacking is a mesoscale connectome. This viewpoint (1) explains the need for a mesoscale connectome in the primate brain (the columnar connectome), (2) presents a new method for acquiring such data rapidly on a large scale, and (3) proposes how one might use such data to achieve a mathematics of brain function. THE COLUMNAR CONNECTOME The Cerebral Cortex Is Composed of Modular Processing Units Termed “Columns” In humans and nonhuman primates, the cerebral cortex occupies a large proportion of brain volume. This remarkable structure is highly organized. Anatomically, it is a two-dimensional Citation: Roe, A. W. (2019). Columnar (2D) sheet, roughly 2mm in thickness, and divided into different cortical areas, each specializ- connectome: toward a mathematics of brain function. Network Neuroscience, ing in some aspect of sensory, motor, cognitive, and limbic function. There is a large literature, 3(3), 779–791. https://doi.org/10.1162/ especially from studies of the nonhuman primate visual cortex, to support the view that the netn_a_00088 cerebral cortex is composed of submillimeter modular functional units, termed “columns” DOI: (Mountcastle, 1997). -
The Nonhuman Primate Neuroimaging & Neuroanatomy
The NonHuman Primate Neuroimaging & Neuroanatomy Project Authors Takuya Hayashi1,2*, Yujie Hou3†, Matthew F Glasser4,5†, Joonas A Autio1†, Kenneth Knoblauch3, Miho Inoue-Murayama6, Tim Coalson4, Essa Yacoub7, Stephen Smith8, Henry Kennedy3,9‡, and David C Van Essen4‡ Affiliations 1RIKEN Center for Biosystems Dynamics Research, Kobe, Japan 2Department of Neurobiology, Kyoto University Graduate School of Medicine, Kyoto, Japan 3Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France 4Department of Neuroscience and 5Radiology, Washington University Medical School, St Louis, MO USA 6Wildlife Research Center, Kyoto University, Kyoto, Japan 7Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA 8Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, UK 9Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences (CAS) Key Laboratory of Primate Neurobiology, CAS, Shanghai, China †‡Equal contributions *Corresponding author Takuya Hayashi Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research 6-7-3 MI R&D Center 3F, Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan Author contributions Takuya Hayashi: Conceptualization, Funding acquisition, Investigation, Formal Analysis, Writing - original draft, review & editing Yujie -
Effects of Experimental Strabismus on the Architecture of Macaque Monkey Striate Cortex
THE JOURNAL OF COMPARATIVE NEUROLOGY 438:300–317 (2001) Effects of Experimental Strabismus on the Architecture of Macaque Monkey Striate Cortex SUZANNE B. FENSTEMAKER,1,2* LYNNE KIORPES,2 AND J. ANTHONY MOVSHON1,2 1Howard Hughes Medical Institute, New York University, New York, New York 10003 2Center for Neural Science, New York University, New York, New York 10003 ABSTRACT Strabismus, a misalignment of the eyes, results in a loss of binocular visual function in humans. The effects are similar in monkeys, where a loss of binocular convergence onto single cortical neurons is always found. Changes in the anatomical organization of primary visual cortex (V1) may be associated with these physiological deficits, yet few have been reported. We examined the distributions of several anatomical markers in V1 of two experimentally strabismic Macaca nemestrina monkeys. Staining patterns in tangential sections were re- lated to the ocular dominance (OD) column structure as deduced from cytochrome oxidase (CO) staining. CO staining appears roughly normal in the superficial layers, but in layer 4C, one eye’s columns were pale. Thin, dark stripes falling near OD column borders are evident in Nissl-stained sections in all layers and in immunoreactivity for calbindin, especially in layers 3 and 4B. The monoclonal antibody SMI32, which labels a neurofilament protein found in pyramidal cells, is reduced in one eye’s columns and absent at OD column borders. The pale SMI32 columns are those that are dark with CO in layer 4. Gallyas staining for myelin reveals thin stripes through layers 2–5; the dark stripes fall at OD column centers.