Connectionism (Artificial Neural Networks) and Dynamical Systems
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Artificial Neural Networks Part 2/3 – Perceptron
Artificial Neural Networks Part 2/3 – Perceptron Slides modified from Neural Network Design by Hagan, Demuth and Beale Berrin Yanikoglu Perceptron • A single artificial neuron that computes its weighted input and uses a threshold activation function. • It effectively separates the input space into two categories by the hyperplane: wTx + b = 0 Decision Boundary The weight vector is orthogonal to the decision boundary The weight vector points in the direction of the vector which should produce an output of 1 • so that the vectors with the positive output are on the right side of the decision boundary – if w pointed in the opposite direction, the dot products of all input vectors would have the opposite sign – would result in same classification but with opposite labels The bias determines the position of the boundary • solve for wTp+b = 0 using one point on the decision boundary to find b. Two-Input Case + a = hardlim(n) = [1 2]p + -2 w1, 1 = 1 w1, 2 = 2 - Decision Boundary: all points p for which wTp + b =0 If we have the weights and not the bias, we can take a point on the decision boundary, p=[2 0]T, and solving for [1 2]p + b = 0, we see that b=-2. p w wT.p = ||w||||p||Cosθ θ Decision Boundary proj. of p onto w proj. of p onto w T T w p + b = 0 w p = -bœb = ||p||Cosθ 1 1 = wT.p/||w|| • All points on the decision boundary have the same inner product (= -b) with the weight vector • Therefore they have the same projection onto the weight vector; so they must lie on a line orthogonal to the weight vector ADVANCED An Illustrative Example Boolean OR ⎧ 0 ⎫ ⎧ 0 ⎫ ⎧ 1 ⎫ ⎧ 1 ⎫ ⎨p1 = , t1 = 0 ⎬ ⎨p2 = , t2 = 1 ⎬ ⎨p3 = , t3 = 1⎬ ⎨p4 = , t4 = 1⎬ ⎩ 0 ⎭ ⎩ 1 ⎭ ⎩ 0 ⎭ ⎩ 1 ⎭ Given the above input-output pairs (p,t), can you find (manually) the weights of a perceptron to do the job? Boolean OR Solution 1) Pick an admissable decision boundary 2) Weight vector should be orthogonal to the decision boundary. -
Artificial Neuron Using Mos2/Graphene Threshold Switching Memristor
University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2018 Artificial Neuron using MoS2/Graphene Threshold Switching Memristor Hirokjyoti Kalita University of Central Florida Part of the Electrical and Electronics Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation Kalita, Hirokjyoti, "Artificial Neuron using MoS2/Graphene Threshold Switching Memristor" (2018). Electronic Theses and Dissertations, 2004-2019. 5988. https://stars.library.ucf.edu/etd/5988 ARTIFICIAL NEURON USING MoS2/GRAPHENE THRESHOLD SWITCHING MEMRISTOR by HIROKJYOTI KALITA B.Tech. SRM University, 2015 A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in the Department of Electrical and Computer Engineering in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Summer Term 2018 Major Professor: Tania Roy © 2018 Hirokjyoti Kalita ii ABSTRACT With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Implementing neuromorphic computing in hardware will be a severe boost for applications involving complex processes such as pattern recognition. Artificial neurons form a critical part in neuromorphic circuits, and have been realized with complex complementary metal–oxide–semiconductor (CMOS) circuitry in the past. Recently, insulator-to-metal-transition (IMT) materials have been used to realize artificial neurons. -
Using the Modified Back-Propagation Algorithm to Perform Automated Downlink Analysis by Nancy Y
Using The Modified Back-propagation Algorithm To Perform Automated Downlink Analysis by Nancy Y. Xiao Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degrees of Bachelor of Science and Master of Engineering in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 1996 Copyright Nancy Y. Xiao, 1996. All rights reserved. The author hereby grants to MIT permission to reproduce and distribute publicly paper and electronic copies of this thesis document in whole or in part, and to grant others the right to do so. A uthor ....... ............ Depar -uter Science May 28, 1996 Certified by. rnard C. Lesieutre ;rical Engineering Thesis Supervisor Accepted by.. F.R. Morgenthaler Chairman, Departmental Committee on Graduate Students OF TECH NOLOCG JUN 111996 Eng, Using The Modified Back-propagation Algorithm To Perform Automated Downlink Analysis by Nancy Y. Xiao Submitted to the Department of Electrical Engineering and Computer Science on May 28, 1996, in partial fulfillment of the requirements for the degrees of Bachelor of Science and Master of Engineering in Electrical Engineering and Computer Science Abstract A multi-layer neural network computer program was developed to perform super- vised learning tasks. The weights in the neural network were found using the back- propagation algorithm. Several modifications to this algorithm were also implemented to accelerate error convergence and optimize search procedures. This neural network was used mainly to perform pattern recognition tasks. As an initial test of the system, a controlled classical pattern recognition experiment was conducted using the X and Y coordinates of the data points from two to five possibly overlapping Gaussian distributions, each with a different mean and variance. -
Cajal's Law of Dynamic Polarization: Mechanism and Design
philosophies Article Cajal’s Law of Dynamic Polarization: Mechanism and Design Sergio Daniel Barberis ID Facultad de Filosofía y Letras, Instituto de Filosofía “Dr. Alejandro Korn”, Universidad de Buenos Aires, Buenos Aires, PO 1406, Argentina; sbarberis@filo.uba.ar Received: 8 February 2018; Accepted: 11 April 2018; Published: 16 April 2018 Abstract: Santiago Ramón y Cajal, the primary architect of the neuron doctrine and the law of dynamic polarization, is considered to be the founder of modern neuroscience. At the same time, many philosophers, historians, and neuroscientists agree that modern neuroscience embodies a mechanistic perspective on the explanation of the nervous system. In this paper, I review the extant mechanistic interpretation of Cajal’s contribution to modern neuroscience. Then, I argue that the extant mechanistic interpretation fails to capture the explanatory import of Cajal’s law of dynamic polarization. My claim is that the definitive formulation of Cajal’s law of dynamic polarization, despite its mechanistic inaccuracies, embodies a non-mechanistic pattern of reasoning (i.e., design explanation) that is an integral component of modern neuroscience. Keywords: Cajal; law of dynamic polarization; mechanism; utility; design 1. Introduction The Spanish microanatomist and Nobel laureate Santiago Ramón y Cajal (1852–1934), the primary architect of the neuron doctrine and the law of dynamic polarization, is considered to be the founder of modern neuroscience [1,2]. According to the neuron doctrine, the nerve cell is the anatomical, physiological, and developmental unit of the nervous system [3,4]. To a first approximation, the law of dynamic polarization states that nerve impulses are exactly polarized in the neuron; in functional terms, the dendrites and the cell body work as a reception device, the axon works as a conduction device, and the terminal arborizations of the axon work as an application device. -
The Churchlands' Neuron Doctrine: Both Cognitive and Reductionist
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/231998559 The Churchlands' neuron doctrine: Both cognitive and reductionist Article in Behavioral and Brain Sciences · October 1999 DOI: 10.1017/S0140525X99462193 CITATIONS READS 2 20 1 author: John Sutton Macquarie University 139 PUBLICATIONS 1,356 CITATIONS SEE PROFILE All content following this page was uploaded by John Sutton on 06 August 2014. The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. The Churchlands’ neuron doctrine: both cognitive and reductionist John Sutton Macquarie University, Sydney, NSW 2109, Australia Behavioral and Brain Sciences, 22 (1999), 850-1. [email protected] http://johnsutton.net/ Commentary on Gold & Stoljar, ‘A Neuron Doctrine in the Philosophy of Neuroscience’, Behavioral and Brain Sciences, 22 (1999), 809-869: full paper and commentaries online at: http://www.stanford.edu/~paulsko/papers/GSND.pdf Abstract: According to Gold and Stoljar, one cannot both consistently be reductionist about psychoneural relations and invoke concepts developed in the psychological sciences. I deny the utility of their distinction between biological and cognitive neuroscience, suggesting that they construe biological neuroscience too rigidly and cognitive neuroscience too liberally. Then I reject their characterization of reductionism: reductions need not go down past neurobiology straight to physics, and cases of partial, local reduction are not neatly distinguishable from cases of mere implementation. Modifying the argument from unification-as-reduction, I defend a position weaker than the radical, but stronger than the trivial neuron doctrine. -
Deep Multiphysics and Particle–Neuron Duality: a Computational Framework Coupling (Discrete) Multiphysics and Deep Learning
applied sciences Article Deep Multiphysics and Particle–Neuron Duality: A Computational Framework Coupling (Discrete) Multiphysics and Deep Learning Alessio Alexiadis School of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UK; [email protected]; Tel.: +44-(0)-121-414-5305 Received: 11 November 2019; Accepted: 6 December 2019; Published: 9 December 2019 Featured Application: Coupling First-Principle Modelling with Artificial Intelligence. Abstract: There are two common ways of coupling first-principles modelling and machine learning. In one case, data are transferred from the machine-learning algorithm to the first-principles model; in the other, from the first-principles model to the machine-learning algorithm. In both cases, the coupling is in series: the two components remain distinct, and data generated by one model are subsequently fed into the other. Several modelling problems, however, require in-parallel coupling, where the first-principle model and the machine-learning algorithm work together at the same time rather than one after the other. This study introduces deep multiphysics; a computational framework that couples first-principles modelling and machine learning in parallel rather than in series. Deep multiphysics works with particle-based first-principles modelling techniques. It is shown that the mathematical algorithms behind several particle methods and artificial neural networks are similar to the point that can be unified under the notion of particle–neuron duality. This study explains in detail the particle–neuron duality and how deep multiphysics works both theoretically and in practice. A case study, the design of a microfluidic device for separating cell populations with different levels of stiffness, is discussed to achieve this aim. -
Neural Network Architectures and Activation Functions: a Gaussian Process Approach
Fakultät für Informatik Neural Network Architectures and Activation Functions: A Gaussian Process Approach Sebastian Urban Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Prof. Dr. rer. nat. Stephan Günnemann Prüfende der Dissertation: 1. Prof. Dr. Patrick van der Smagt 2. Prof. Dr. rer. nat. Daniel Cremers 3. Prof. Dr. Bernd Bischl, Ludwig-Maximilians-Universität München Die Dissertation wurde am 22.11.2017 bei der Technischen Universtät München eingereicht und durch die Fakultät für Informatik am 14.05.2018 angenommen. Neural Network Architectures and Activation Functions: A Gaussian Process Approach Sebastian Urban Technical University Munich 2017 ii Abstract The success of applying neural networks crucially depends on the network architecture being appropriate for the task. Determining the right architecture is a computationally intensive process, requiring many trials with different candidate architectures. We show that the neural activation function, if allowed to individually change for each neuron, can implicitly control many aspects of the network architecture, such as effective number of layers, effective number of neurons in a layer, skip connections and whether a neuron is additive or multiplicative. Motivated by this observation we propose stochastic, non-parametric activation functions that are fully learnable and individual to each neuron. Complexity and the risk of overfitting are controlled by placing a Gaussian process prior over these functions. The result is the Gaussian process neuron, a probabilistic unit that can be used as the basic building block for probabilistic graphical models that resemble the structure of neural networks. -
A Connectionist Model Based on Physiological Properties of the Neuron
Proceedings of the International Joint Conference IBERAMIA/SBIA/SBRN 2006 - 1st Workshop on Computational Intelligence (WCI’2006), Ribeir˜aoPreto, Brazil, October 23–28, 2006. CD-ROM. ISBN 85-87837-11-7 A Connectionist Model based on Physiological Properties of the Neuron Alberione Braz da Silva Joao˜ Lu´ıs Garcia Rosa Ceatec, PUC-Campinas Ceatec, PUC-Campinas Campinas, SP, Brazil Campinas, SP, Brazil [email protected] [email protected] Abstract 2 Intraneuron Signaling Recent research on artificial neural network models re- 2.1 The biological neuron and the intra- gards as relevant a new characteristic called biological neuron signaling plausibility or realism. Nevertheless, there is no agreement about this new feature, so some researchers develop their The intraneuron signaling is based on the principle of own visions. Two of these are highlighted here: the first is dynamic polarization, proposed by Ramon´ y Cajal, which related directly to the cerebral cortex biological structure, establishes that “electric signals inside a nervous cell flow and the second focuses the neural features and the signaling only in a direction: from neuron reception (often the den- between neurons. The proposed model departs from the pre- drites and cell body) to the axon trigger zone” [5]. vious existing system models, adopting the standpoint that a Based on physiological evidences of principle of dy- biologically plausible artificial neural network aims to cre- namic polarization the signaling inside the neuron is per- ate a more faithful model concerning the biological struc- formed by four basic elements: Receptive, responsible for ture, properties, and functionalities of the cerebral cortex, input signals; Trigger, responsible for neuron activation not disregarding its computational efficiency. -
Artificial Neural Networks· T\ Briet In!Toquclion
GENERAL I ARTICLE Artificial Neural Networks· t\ BrieT In!TOQuclion Jitendra R Raol and Sunilkumar S Mankame Artificial neural networks are 'biologically' inspired net works. They have the ability to learn from empirical datal information. They find use in computer science and control engineering fields. In recent years artificial neural networks (ANNs) have fascinated scientists and engineers all over the world. They have the ability J R Raol is a scientist with to learn and recall - the main functions of the (human) brain. A NAL. His research major reason for this fascination is that ANNs are 'biologically' interests are parameter inspired. They have the apparent ability to imitate the brain's estimation, neural networks, fuzzy systems, activity to make decisions and draw conclusions when presented genetic algorithms and with complex and noisy information. However there are vast their applications to differences between biological neural networks (BNNs) of the aerospace problems. He brain and ANN s. writes poems in English. A thorough understanding of biologically derived NNs requires knowledge from other sciences: biology, mathematics and artifi cial intelligence. However to understand the basics of ANNs, a knowledge of neurobiology is not necessary. Yet, it is a good idea to understand how ANNs have been derived from real biological neural systems (see Figures 1,2 and the accompanying boxes). The soma of the cell body receives inputs from other neurons via Sunilkumar S Mankame is adaptive synaptic connections to the dendrites and when a neuron a graduate research student from Regional is excited, the nerve impulses from the soma are transmitted along Engineering College, an axon to the synapses of other neurons. -
Richard Scheller and Thomas Südhof Receive the 2013 Albert Lasker Basic Medical Research Award
Richard Scheller and Thomas Südhof receive the 2013 Albert Lasker Basic Medical Research Award Jillian H. Hurst J Clin Invest. 2013;123(10):4095-4101. https://doi.org/10.1172/JCI72681. News Neural communication underlies all brain activity. It governs our thoughts, feelings, sensations, and actions. But knowing the importance of neural communication does not answer a central question of neuroscience: how do individual neurons communicate? We know that communication between two neurons occurs at specialized cell junctions called synapses, at which two communicating neurons are separated by the synaptic cleft. The presynaptic neuron releases chemicals, known as neurotransmitters, into the synaptic cleft in which neurotransmitters bind to receptors on the surface of the postsynaptic neuron. Neurotransmitter release occurs in response to an action potential within the sending neuron that induces depolarization of the nerve terminal and causes an influx of calcium. Calcium influx triggers the release of neurotransmitters through a specialized form of exocytosis in which neurotransmitter-filled vesicles fuse with the plasma membrane of the presynaptic nerve terminal in a region known as the active zone, spilling neurotransmitter into the synaptic cleft. By the 1950s, it was clear that brain function depended on chemical neurotransmission; however, the molecular activities that governed neurotransmitter release were virtually unknown until the early 1990s. This year, the Lasker Foundation honors Richard Scheller (Genentech) and Thomas Südhof (Stanford University School of Medicine) for their “discoveries concerning the molecular machinery and regulatory mechanisms that underlie the rapid release of neurotransmitters.” Over the course of two decades, Scheller […] Find the latest version: https://jci.me/72681/pdf News Richard Scheller and Thomas Südhof receive the 2013 Albert Lasker Basic Medical Research Award Neural communication underlies all Setting the stage um-driven action potentials elicited neu- brain activity. -
From the Neuron Doctrine to Neural Networks
LINK TO ORIGINAL ARTICLE LINK TO INITIAL CORRESPONDENCE other hand, one of my mentors, David Tank, argued that for a true understanding of a On testing neural network models neural circuit we should be able to actu- ally build it, which is a stricter definition Rafael Yuste of a successful theory (D. Tank, personal communication) Finally, as mentioned in In my recent Timeline article, I described existing neural network models have enough the Timeline article, one will also need to the emergence of neural network models predictive value to be considered valid or connect neural network models to theories as an important paradigm in neuroscience useful for explaining brain circuits.” (REF. 1)). and facts at the structural and biophysical research (From the neuron doctrine to neu- There are many exciting areas of progress levels of neural circuits and to those in cog- ral networks. Nat. Rev. Neurosci. 16, 487–497 in current neuroscience detailing phenom- nitive sciences as well, for proper ‘scientific (2015))1. In his correspondence (Neural enology that is consistent with some neural knowledge’ to occur in the Kantian sense. networks in the future of neuroscience network models, some of which I tried to research. Nat. Rev. Neurosci. http://dx.doi. summarize and illustrate, but at the same Rafael Yuste is at the Neurotechnology Center and org/10.1038/nrn4042 (2015))2, Rubinov time we are still far from a rigorous demon- Kavli Institute of Brain Sciences, Departments of provides some thoughtful comments about stration of any neural network model with Biological Sciences and Neuroscience, Columbia University, New York, New York 10027, USA. -
Neuron: Electrolytic Theory & Framework for Understanding Its
Neuron: Electrolytic Theory & Framework for Understanding its operation Abstract: The Electrolytic Theory of the Neuron replaces the chemical theory of the 20th Century. In doing so, it provides a framework for understanding the entire Neural System to a comprehensive and contiguous level not available previously. The Theory exposes the internal workings of the individual neurons to an unprecedented level of detail; including describing the amplifier internal to every neuron, the Activa, as a liquid-crystalline semiconductor device. The Activa exhibits a differential input and a single ended output, the axon, that may bifurcate to satisfy its ultimate purpose. The fundamental neuron is recognized as a three-terminal electrolytic signaling device. The fundamental neuron is an analog signaling device that may be easily arranged to process pulse signals. When arranged to process pulse signals, its axon is typically myelinated. When multiple myelinated axons are grouped together, the group are labeled “white matter” because of their translucent which scatters light. In the absence of myelination, groups of neurons and their extensions are labeled “dark matter.” The dark matter of the Central Nervous System, CNS, are readily divided into explicit “engines” with explicit functional duties. The duties of the engines of the Neural System are readily divided into “stages” to achieve larger characteristic and functional duties. Keywords: Activa, liquid-crystalline, semiconductors, PNP, analog circuitry, pulse circuitry, IRIG code, 2 Neurons & the Nervous System The NEURONS and NEURAL SYSTEM 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.