DOCSLIB.ORG

A New Type of Neurons for Machine Learning

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A New Type of Neurons for Machine Learning > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 A New Type of Neurons for Machine Learning Fenglei Fan, Wenxiang Cong, Ge Wang Biomedical Imaging Center, BME/CBIS Rensselaer Polytechnic Institute, Troy, New York, USA components contribute complementary strengths and Abstract — In machine learning, the use of an artificial neural compensate for others’ weaknesses [11]. Most of cell types are network is the mainstream approach. Such a network consists of highly dedicated to desirable forms and functions. Synthetic layers of neurons. These neurons are of the same type biological research may enhance the biological diversity characterized by the two features: (1) an inner product of an input further. This observation makes us wonder if we could have vector and a matching weighting vector of trainable parameters and (2) a nonlinear excitation function. Here we investigate the multiple types of neurons for machine learning either in possibility of replacing the inner product with a quadratic general or for specific tasks. function of the input vector, thereby upgrading the 1st order neuron to the 2nd order neuron, empowering individual neurons, As the first step along this direction, here we investigate the and facilitating the optimization of neural networks. Also, possibility of replacing the inner product with a quadratic numerical examples are provided to illustrate the feasibility and function of the input vector, thereby upgrading the 1st order merits of the 2nd order neurons. Finally, further topics are neuron to the 2nd order neuron, empowering individual neurons, discussed. and facilitating the optimization of neural networks. Index Terms — Machine learning, artificial neural network The model of the current single neurons, which are also referred (ANN), 2nd order neuron, convolutional neural network (CNN). to as perceptrons, has been applied to solve linearly separable problems. For linearly inseparable tasks, multi-layers of I. INTRODUCTION neurons are needed to perform multi-scale nonlinear analysis. N the field of machine learning, artificial neural networks In other words, existing neurons can only perform linear I (ANNs) especially deep neural networks (CNNs) have classification individually, and the linearly-limited cellular recently achieved remarkable successes in various types of function can be only enhanced via cellular interconnection into applications such as classification, unsupervised learning, an artificial organism. Our curiosity is to produce such an prediction, image processing and analysis [1-3]. Excited by the artificial organism with neurons capable of performing tremendous potential of machine learning, major efforts are quadratic classification. being made to improve machine learning methods [4]. An In the next section, we describe the 2nd order neurons. Given n important aspect of the methodological research is how to inputs, the new type of neurons could have 3n parameters to optimize the topology of a neural network. For example, approximate a general quadratic function, or have n(n+1)/2 generative adversarial networks (GANs) were proposed to degrees of freedom to admit any quadratic function. utilize some deep results of game theory. In particular, the Furthermore, we formulate how to train the 2nd order neuron. In Wasserstein distance was introduced for effective, efficient and the third section, we present numerical simulation results for stable performance of GAN [5, 6]. fuzzy logic operations. In the last section, we discuss relevant To our best knowledge, all ANNs/CNNs are currently issues and conclude the paper. constructed with neurons of the same type characterized by the two features: (1) an inner product of an input vector and a II. SECOND ORDER MODEL AND OPTIMIZATION matching weighting vector of trainable parameters and (2) a The structure of a current neuron is shown in Fig. 1, where we nonlinear excitation function [7-9]. Although these neurons can define w0 := b and x0 =1. The linear function of the input be interconnected to approximate any general function, the vector produce the output [8]: topology of the network is not unique. On another side of this non-uniqueness, our hypothesis is that the type of neurons n f() x wii x b (1) suitable for general machine learning is not unique either, i1 which is a new dimension of machine learning research. and then fx() will be nonlinearly processed, such as by a The above hypothesis is well motivated. It is commonly known sigmoid function. Clearly, the single neuron can separate two that the current structure of artificial neurons was inspired by sets of inputs that are linearly separable. In contrast, for linearly the inner working of biological neurons. Biologically speaking, inseparable groups of inputs, the single neuron is subject to a core principle is that diversity brings synergy and prosperity classification errors. For example, a single neuron is incapable at all levels. In biology, multiple types of cells are available to of simulating the function of the XOR gate. make various organisms [10]. Indeed, many genomic We introduce a new type of neurons in Fig. 2 where the input vector is turned into two inner products and one norm term for This work is partially supported by the Clark & Crossan Endowment Fund at Rensselaer Polytechnic Institute, Troy, New York, USA. summation before feeding to the nonlinear excitation function. > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 Again, for compact notation, we define that wb: , E n 01r (())()h x y w x b bxi i ig i 2 wb02g : and =1. Then, the output function is expressed as: 1 i1 n n n E n 2 (())()h x y w x b fx( ) ( wxbir i 12 )( wxb ig i ) wxc ib i + . i i ir i 1 bxi1 i1 i 1 i 1 2 (2) E In this embodiment, the threshold is chosen by a sigmoid (())h xii y function. Eq. (2) is quadric, and has the current neuron type as a cx special case. Due to its added nonlinearity, our proposed neuron A general optimization flowchart is in Fig. 3. is intrinsically superior to the current neuron in terms of representation power such as for classification. The training algorithm can be formulated for the proposed 2nd order neuron as follows, assuming the sigmoid function [8] 1 ()x ， (3) 1 exp( x ) where β=1 in this pilot study. Let us denote a training dataset of m samples by Xk x k,,, x k x k , along with the ideal Fig. 1. Current structure of an artificial neuron consisting of a linear 12 n inner product and a nonlinear excitation function. output data y k , km 1,2, , . Then, the output of the 2nd order neuron can be modeled as k h X, wr , w g , w b , b12 , b f x n n n (4) k k k 2 wir x i b12 w ig x i b w ib x i +. c i1 i 1 i 1 Let us define the error function as m 2 1 kk Ewwwbb r, g , b ,1 , 2 hXww , r , g , w b , bb 1 , 2 y (5) Fig. 2. Exemplary structure of our proposed artificial neuron 2 k 1 consisting of a quadratic form and a nonlinear excitation function. The error function depends on the structural parameters: b1 ，b2 ，c , wr w12 r,,, w r w nr ，wg w12 g,,, w g w ng and wb w12 b,,, w b w nb , The goal of machine learning is to find optimal parameters to minimize the objective function [9]. The optimal parameters can be found using the gradient descent method with an appropriate initial guess. Thus, we can iteratively update wr , E w , w , , b and c in the form of , where x g b 2 0 denotes a generic variable of the objective function, and the step size is typically set between zero and one. The gradient of the object function for any sample can be computed as follows: E n Fig. 3. Flowchart for training the 2nd order neuron in terms of its (())()h x y x w x b structural parameters (weights and offsets). Note that we plan to wxi i i ig i 2 ir i1 improve this iterative process for higher efficiency. E n (())()h x y x w x b i i i ir i 1 III. NUMERICAL RESULTS wxig i1 nd E In our pilot study, the proposed 2 order neuron of two input 2(h ( x ) y ) w x variables was individually applied in fuzzy logic experiments. wxi i ib i ib Extending classic Boolean logic, fuzzy logic was extensively > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 3 studied and applied over the past decades, which manipulates vague or imprecise logic statements. Compared with Boolean logic, the value of a fuzzy logic variable is on the interval [0, 1], since a practical judgement may not be purely black (false or 0) or white (true or 1). The fuzziness of a logic variable can be easily reflected in a continuous variable by its closeness to either 0 or 1. For visualization, the color map “cool” in MATLAB was used to represent the functional value at every point. We used “o” for 0 and “+” for 1. With the proposed 2nd order neuron, the training process kept refining a quadratic contour to separate labeled points for the highest classification accuracy. By the nature of the 2nd order neuron, the contour can be two lines or curves including parabolic and elliptical boundaries. A. XOR Gate First, the training dataset was simply the XOR logic table, which is not separable by a single neuron of the conventional type. To implement the XOR-gate operation with our proposed Fig. 5. XOR-like function computed by the proposed 2nd order neuron after 100 nd 2 order neuron, the initial parameters could be randomly iterations.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Artificial Neuron Using Vertical Mos2/Graphene Threshold
    www.nature.com/scientificreports OPEN Artifcial Neuron using Vertical MoS2/Graphene Threshold Switching Memristors Received: 11 June 2018 Hirokjyoti Kalita1,2, Adithi Krishnaprasad1,2, Nitin Choudhary1, Sonali Das1, Durjoy Dev1,2, Accepted: 6 November 2018 Yi Ding1,3, Laurene Tetard1,4, Hee-Suk Chung 5, Yeonwoong Jung1,2,3 & Tania Roy1,2,3 Published: xx xx xxxx 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 image processing and pattern recognition. Artifcial neurons form a critical part in neuromorphic circuits, and have been realized with complex complementary metal–oxide–semiconductor (CMOS) circuitry in the past. Recently, metal-insulator- transition materials have been used to realize artifcial neurons. Although memristors have been implemented to realize synaptic behavior, not much work has been reported regarding the neuronal response achieved with these devices. In this work, we use the volatile threshold switching behavior of a vertical-MoS2/graphene van der Waals heterojunction system to produce the integrate-and-fre response of a neuron. We use large area chemical vapor deposited (CVD) graphene and MoS2, enabling large scale realization of these devices. These devices can emulate the most vital properties of a neuron, including the all or nothing spiking, the threshold driven spiking of the action potential, the post-fring refractory period of a neuron and strength modulated frequency response. These results show that the developed artifcial neuron can play a crucial role in neuromorphic computing. Understanding the complex and overwhelming functioning of the human brain has always fascinated scientists and researchers for biological felds and beyond.
    [Show full text]
  • Understanding Deep Neural Networks
    Bachelor thesis (Computer Science BSc) Understanding Deep Neural Networks Authors David Kempf Lino von Burg Main supervisors Dr. Thilo Stadelmann Prof. Dr. Olaf Stern Date 08.06.2018 DECLARATION OF ORIGINALITY Bachelor’s Thesis at the School of Engineering By submitting this Bachelor’s thesis, the undersigned student confirms that this thesis is his/her own work and was written without the help of a third party. (Group works: the performance of the other group members are not considered as third party). The student declares that all sources in the text (including Internet pages) and appendices have been correctly disclosed. This means that there has been no plagiarism, i.e. no sections of the Bachelor thesis have been partially or wholly taken from other texts and represented as the student’s own work or included without being correctly referenced. Any misconduct will be dealt with according to paragraphs 39 and 40 of the General Academic Regulations for Bachelor’s and Master’s Degree courses at the Zurich University of Applied Sciences (Rahmenprüfungsordnung ZHAW (RPO)) and subject to the provisions for disciplinary action stipulated in the University regulations. City, Date: Signature: …………………………………………………………………….. …………………………..………………………………………………………... …………………………..………………………………………………………... …………………………..………………………………………………………... The original signed and dated document (no copies) must be included after the title sheet in the ZHAW version of all Bachelor thesis submitted. Zurich University of Applied Sciences Abstract Artificial Intelligence (AI) comprises a wealth of distinct problem solving methods and attempts with its various subfields to tackle different classes of challenges. During the last decade, huge advances have been made in the field and, as a consequence, AI has achieved great results in machine translation and computer vision tasks.
    [Show full text]
  • Artificial Neuron-Glia Networks Learning Approach Based on Cooperative Coevolution
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Repositorio da Universidade da Coruña ARTIFICIAL NEURON-GLIA NETWORKS LEARNING APPROACH BASED ON COOPERATIVE COEVOLUTION PABLO MESEJO Department of Information Engineering, University of Parma, Parma, 43124, Italy ISIT-UMR 6284 CNRS, University of Auvergne, Clermont-Ferrand, 63000, France OSCAR IBA´NEZ~ European Centre for Soft Computing, Mieres, 33600, Spain Dept. of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Granada, 18071, Spain ENRIQUE FERNANDEZ-BLANCO,´ FRANCISCO CEDRON,´ ALEJANDRO PAZOS, and ANA B. PORTO-PAZOS* Department of Information and Communications Technologies, University of A Coru~na, A Coru~na,15071, Spain Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. How- ever, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configura- tion time-consuming, error-prone, biased and problem dependent. Thus, in this article, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem.
    [Show full text]
  • Art in Machine Learning
    1 Creativity in Machine Learning w0 x0 w1 x1 w2 Martin Thoma x2 Σ ' w3 E-Mail: [email protected] x3 . wn xn Abstract—Recent machine learning techniques can be modified (a) Example of an artificial neuron unit.(b) A visualization of a simple feed- to produce creative results. Those results did not exist before; it xi are the input signals and wi are forward neural network. The 5 in- is not a trivial combination of the data which was fed into the weights which have to get learned. put nodes are red, the 2 bias nodes machine learning system. The obtained results come in multiple Each input signal gets multiplied are gray, the 3 hidden units are forms: As images, as text and as audio. with its weight, everything gets green and the single output node summed up and the activation func- is blue. This paper gives a high level overview of how they are created tion ' is applied. and gives some examples. It is meant to be a summary of the current work and give people who are new to machine learning Fig. 1: Neural networks are based on simple units which get some starting points. combined to complex networks. I. INTRODUCTION This means that machine learning programs adjust internal parameters to fit the data they are given. Those computer According to [Gad06] creativity is “the ability to use your programs are still developed by software developers, but the imagination to produce new ideas, make things etc.” and developer writes them in a way which makes it possible to imagination is “the ability to form pictures or ideas in your adjust them without having to re-program everything.
    [Show full text]
  • Hybrid Memristor-CMOS Neurons for in Situ Learning in Fully Hardware Memristive Spiking Neural Networks
    Hybrid memristor-CMOS neurons for in situ learning in fully hardware memristive spiking neural networks Xumeng Zhang Fudan University Jian Lu Institute of Microelectronics of the Chinese Academy of Science Rui Wang Chinese Academy of Sciences Jinsong Wei Institute of Microelectronics of the Chinese Academy of Science Tuo Shi Chinese Academy of Sciences Chunmeng Dou Institute of Microelectronics of the Chinese Academy of Science zuheng Wu Institute of Microelectronics Dashan Shang Chinese Academy of Sciences https://orcid.org/0000-0003-3573-8390 Guozhong Xing Institute of Microelectronics of the Chinese Academy of Science Qi Liu Fudan University Ming Liu ( [email protected] ) Key Laboratory of Microelectronics Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Science Article Keywords: Spiking neural network, memristor-based neurons, hybrid memristor-CMOS neurons Posted Date: July 27th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-43977/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published at Science Bulletin on April 1st, 2021. See the published version at https://doi.org/10.1016/j.scib.2021.04.014. Hybrid memristor-CMOS neurons for in situ learning in fully hardware memristive spiking neural networks Xumeng Zhang#1,2,3, Jian Lu#2, Rui Wang2,3, Jinsong Wei2, Tuo Shi2,4, Chunmeng Dou2,3, Zuheng Wu2,3, Dashan Shang2,3, Guozhong Xing2,3, Qi Liu*1,2, and Ming Liu1,2 1Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China, 2Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China, 3University of Chinese Academy of Sciences, Beijing 100049, China, 4Zhejiang Laboratory, Hangzhou 311122, China.
    [Show full text]