A Batch-Language, Vector-Based Neural Network Simulator Motivation: - general computer languages (e.g. C) lead to complex code - neural network simulators.

Slides:



Advertisements
Similar presentations
Introduction to Neural Networks 2. Overview  The McCulloch-Pitts neuron  Pattern space  Limitations  Learning.
Advertisements

Session Objectives# 24 COULD code the solution for an algorithm
Computational Electronics Generalized Monte Carlo Tool for Investigating Low-Field and High Field Properties of Materials Using Non-parabolic Band Structure.
Paper Discussion: “Simultaneous Localization and Environmental Mapping with a Sensor Network”, Marinakis et. al. ICRA 2011.
Brain-like design of sensory-motor programs for robots G. Palm, Uni-Ulm.
Evolutionary Algorithms Simon M. Lucas. The basic idea Initialise a random population of individuals repeat { evaluate select vary (e.g. mutate or crossover)
Presenting: Itai Avron Supervisor: Chen Koren Characterization Presentation Spring 2005 Implementation of Artificial Intelligence System on FPGA.
Multi Layer Perceptrons (MLP) Course website: The back-propagation algorithm Following Hertz chapter 6.
Neural Networks Primer Dr Bernie Domanski The City University of New York / CSI 2800 Victory Blvd 1N-215 Staten Island, New York 10314
Debugging Tips for Programmers. Outline Script debugging Script debugging –C shell –BASH/Bourne/Korn shell tips Compiled language debugging Compiled language.
© 2004 The MathWorks, Inc. 1 MATLAB for C/C++ Programmers Support your C/C++ development using MATLAB’s prebuilt graphics functions and trusted numerics.
Soft Computing Lecture 18 Foundations of genetic algorithms (GA). Using of GA.
5.5 Learning algorithms. Neural Network inherits their flexibility and computational power from their natural ability to adjust the changing environments.
1. Produce a folio of tasks that demonstrate a progression in acquiring and applying programming knowledge (ie. learn Visual Basic) 2. Learn about computer.
STUDY, MODEL & INTERFACE WITH MOTOR CORTEX Presented by - Waseem Khatri.
JAVA Java is a programming language and computing platform first released by Sun Microsystems in It was first developed by James Gosling at Sun Microsystems,
Back-Propagation MLP Neural Network Optimizer ECE 539 Andrew Beckwith.
An informal description of artificial neural networks John MacCormick.
Computational Thinking in K-12 and Scalable Game Design Michael Shuffett.
Building high-level features using large-scale unsupervised learning Anh Nguyen, Bay-yuan Hsu CS290D – Data Mining (Spring 2014) University of California,
The Perceptron. Perceptron Pattern Classification One of the purposes that neural networks are used for is pattern classification. Once the neural network.
MATLAB Harri Saarnisaari, Part of Simulations and Tools for Telecommunication Course.
Artificial Intelligence By Michelle Witcofsky And Evan Flanagan.
Map-Reduce for Machine Learning on Multicore C. Chu, S.K. Kim, Y. Lin, Y.Y. Yu, G. Bradski, A.Y. Ng, K. Olukotun (NIPS 2006) Shimin Chen Big Data Reading.
OCR GCSE Computing © Hodder Education 2013 Slide 1 OCR GCSE Computing Python programming 1: Introduction.
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology O( ㏒ 2 M) Self-Organizing Map Algorithm Without Learning.
CS307P-SYSTEM PRACTICUM CPYNOT. B13107 – Amit Kumar B13141 – Vinod Kumar B13218 – Paawan Mukker.
Kaifeng Chen Institute for Theoretical Physics Synthetic Biology with Engineering Tools 1 Francis Chen.
Brain-Machine Interface (BMI) System Identification Siddharth Dangi and Suraj Gowda BMIs decode neural activity into control signals for prosthetic limbs.
1.7 Linear Independence. in R n is said to be linearly independent if has only the trivial solution. in R n is said to be linearly dependent if there.
Application Development in Engineering Optimization with Matlab and External Solvers Aalto University School of Engineering.
Neural Networks. Background - Neural Networks can be : Biological - Biological models Artificial - Artificial models - Desire to produce artificial systems.
CSC321: Introduction to Neural Networks and Machine Learning Lecture 17: Boltzmann Machines as Probabilistic Models Geoffrey Hinton.
Dimensions of Neural Networks Ali Akbar Darabi Ghassem Mirroshandel Hootan Nokhost.
The Matrix Equation A x = b (9/16/05) Definition. If A is an m  n matrix with columns a 1, a 2,…, a n and x is a vector in R n, then the product of A.
語音訊號處理之初步實驗 NTU Speech Lab 指導教授: 李琳山 助教: 熊信寬
How do you get here?
Ghent University Backpropagation for Population-Temporal Coded Spiking Neural Networks July WCCI/IJCNN 2006 Benjamin Schrauwen and Jan Van Campenhout.
1 Neural Networks MUMT 611 Philippe Zaborowski April 2005.
REVIEW Linear Combinations Given vectors and given scalars
Outline Of Today’s Discussion
Optical RESERVOIR COMPUTING
基于多核加速计算平台的深度神经网络 分割与重训练技术
RADIAL BASIS FUNCTION NEURAL NETWORK DESIGN
Adavanced Numerical Computation 2008, AM NDHU
1-1 Logic and Syntax A computer program is a solution to a problem.
Extreme Learning Machine
Unit 3 lesson 2&3 The Need For Algorithms- Creativity in Algorithms
MATLAB Basics Nafees Ahmed Asstt. Professor, EE Deptt DIT, DehraDun.
CSC321: Neural Networks Lecture 19: Boltzmann Machines as Probabilistic Models Geoffrey Hinton.
Ad-hoc On-demand Distance Vector
Learning Feature Mappings Using Evolutionary Computation
Today Is S.T.E.M. Day.
Chapter 7: Cortical maps and competitive population coding
Blind Signal Separation using Principal Components Analysis
Physics-based simulation for visual computing applications
Covariation Learning and Auto-Associative Memory
Demonstration of STDP based Neural Networks on an FPGA
network of simple neuron-like computing elements
The use of Neural Networks to schedule flow-shop with dynamic job arrival ‘A Multi-Neural Network Learning for lot Sizing and Sequencing on a Flow-Shop’
Lecture 16: Recurrent Neural Networks (RNNs)
Word2Vec.
Backpropagation and Neural Nets
Everything in terms of x Disc Everything in terms of x.
Hour of Code Code.org/lightbot
Computing as Fast as an Engineer can Think
Derivatives and Gradients
Madhav Nandipati pd. 6 Third Quarter Presentation
Presentation transcript:

A Batch-Language, Vector-Based Neural Network Simulator Motivation: - general computer languages (e.g. C) lead to complex code - neural network simulators are inflexible (e.g. SNNS)‏ Idea: - computation and learning in cortical areas are stereotypical - do everything by combining vector and matrix operations Solution: a simulator which is available at

Information Flow

Shell Commands

GUI “activations” weights a neuron’s receptive field the network activity vector t 2 t 3...

Language File: Network Setup

Language File: Algorithm