CSE/NEUBEH 528 Modeling Synapses and Networks (Chapter 7)

Size: px
Start display at page:

Download "CSE/NEUBEH 528 Modeling Synapses and Networks (Chapter 7)"

Transcription

1 CSE/NEUBEH 528 Modeling Synape and Nework (Chaper 7) Iage fro Wikiedia Coon 1 Lecure figure are fro Dayan & Ao ook Coure Suary (hu far) F Neural Encoding Wha ake a neuron fire? (STA, covariance analyi) Poion odel of piking F Neural Decoding Spike-rain aed decoding of iulu Siulu Dicriinaion aed on firing rae Populaion decoding (Bayeian eiaion) F Single Neuron Model RC circui odel of erane Inegrae-and-fire odel Conducance-aed Model 2

2 Today Agenda F Copuaion in Nework of Neuron Modeling ynapic inpu Fro piking o firing-rae aed nework Feedforward Nework Mulilayer Nework 3 How do neuron connec o for nework? Uing ynape! Iage Source: Wikiedia Coon 4

3 Synape on an acual neuron Iage Credi: Kennedy la, Calech. hp:// 5 Wha do ynape do? Spike Increae or decreae poynapic erane poenial Iage Source: Wikiedia Coon 6

4 An Exciaory Synape Spike Inpu pike Neuroranier releae (e.g., Gluaae) Bind o ion channel recepor Ion channel open Na+ influx Depolarizaion due o EPSP (exciaory poynapic poenial) 7 Iage Source: Wikiedia Coon An Inhiiory Synape Spike Inpu pike Neuroranier releae (e.g., GABA) Bind o ion channel recepor Ion channel open Cl- influx Hyperpolarizaion due o IPSP (inhiiory poynapic poenial) Iage Source: Wikiedia Coon 8

5 We wan a copuaional odel of he effec of a ynape on he erane poenial V Synape V How do we do hi? 9 Flahack Merane Model V = r c = R C i he erane ie conan c dv d dv d ( V E r ( V E ) I L ) Ie, or equivalenly: A L e R 10 Iage Source: Dayan & Ao exook

6 How do we odel he effec of a ynape on he erane poenial V? Synape? 11 Hin! Hodgkin-Huxley Model dv ir IeR d i (1/ r )( V E ) g L n ( V E ) g 3 h( V E 4 K, ax K Na,ax Na E L = -54 V, E K = -77 V, E Na = +50 V K Na ) 12 Iage Source: Dayan & Ao exook

7 Modeling Synapic Inpu Synape V Synapic conducance dv ( V EL) r g( V E) IeR d g g P P,ax rel Proailiy of poynapic channel opening (= fracion of channel opened) Proailiy of ranier releae given an inpu pike 13 Baic Synape Model F Aue P rel = 1 F Model he effec of a ingle pike inpu on P F Kineic Model of poynapic channel: Cloed dp d (1 P ) P fracion of channel opened Open Opening rae Cloing rae Fracion of channel cloed Fracion of channel open 14

8 Wha doe P look like over ie given a pike? ) e Exponenial funcion ) give reaonale fi for oe ynape Oher can e fi uing Alpha funcion: K P ax peak ( ) e 15 0 peak Linear Filer Model of a Synape Inpu Spike Train () Synape () = i δ(- i ) ( i are he inpu pike ie, δ = dela funcion) Filer for ynape = ) Synapic conducance a : g ( ) g g,ax,ax i ) i ) ( ) d 16

9 Exaple: Nework of Inegrae-and-Fire Neuron Exciaory ynape (E = 0 V) Inhiiory ynape (E = -80 V) Synchrony! Each neuron: dv d Synape : Alpha funcion odel ( V E ) r g ( )( V E ) I peak 10 L e R E L 70 V V 54 V hreh 17 Modeling Nework of Neuron F Opion 1: Ue piking neuron Advanage: Model copuaion and learning aed on: Spike Tiing Spike Correlaion/Synchrony eween neuron Diadvanage: Copuaionally expenive F Opion 2: Ue neuron wih firing-rae oupu (real valued oupu) Advanage: Greaer efficiency, cale well o large nework Diadvanage: Ignore pike iing iue F Queion: How are hee wo approache relaed? 18

10 Recall: Linear Filer Model of a Synape Synape Inpu Spike Train () () = i δ(- i ) ( i are he inpu pike ie, δ = dela funcion) Filer for ynape = ) Synapic conducance a : g ( ) g g,ax,ax i ) i ) ( ) d 19 Fro a Single Synape o Muliple Synape Synapic weigh w 1 w N Spike rain 1 () N () Toal ynapic curren I I ( ) N 1 N 1 I ( ) ( ) w ) ( ) d 20

11 Fro Spiking o Firing Rae Model Synapic weigh w 1 w N Spike rain 1 () N () Firing rae u 1 () u N () Toal ynapic curren I ( ) N 1 N 1 w w ) ( ) d Spike rain () ) u ( ) d Firing rae u () 21 Siplifying he Inpu Curren Equaion Synapic weigh w 1 w N Weigh vecor w Firing rae u 1 () u N () Inpu vecor u Suppoe ynapic filer K i exponenial: Differeniaing I ( ) w ) u ( ) d w.r.. ie, we ge di d I I w u w u ) 1 e 22

12 General Firing-Rae-Baed Nework Model Oupu firing rae change like hi: Inpu curren change like hi: r dv d di d v F( I ( )) I w u F i he acivaion funcion Wha happen when:?? r Saic inpu? r 23 Nex Cla: Nework F To Do: Hoework 3 Finalize a final projec opic and parner() Eail Raj, Adrienne and Rich your opic and parner, or ak o e aigned o a ea 24

CSE/NEURO 528 Lecture 13: Reinforcement Learning & Course Review (Chapter 9)

CSE/NEURO 528 Lecture 13: Reinforcement Learning & Course Review (Chapter 9) CSE/NEURO 528 Lecure 13: Reinforceen Learning & Course Review Chaper 9 Aniaion: To Creed, SJU 1 Early Resuls: Pavlov and his Dog F Classical Pavlovian condiioning experiens F Training: Bell Food F Afer:

More information

Linear Time-invariant systems, Convolution, and Cross-correlation

Linear Time-invariant systems, Convolution, and Cross-correlation Linear Time-invarian sysems, Convoluion, and Cross-correlaion (1) Linear Time-invarian (LTI) sysem A sysem akes in an inpu funcion and reurns an oupu funcion. x() T y() Inpu Sysem Oupu y() = T[x()] An

More information

To become more mathematically correct, Circuit equations are Algebraic Differential equations. from KVL, KCL from the constitutive relationship

To become more mathematically correct, Circuit equations are Algebraic Differential equations. from KVL, KCL from the constitutive relationship Laplace Tranform (Lin & DeCarlo: Ch 3) ENSC30 Elecric Circui II The Laplace ranform i an inegral ranformaion. I ranform: f ( ) F( ) ime variable complex variable From Euler > Lagrange > Laplace. Hence,

More information

Single Phase Line Frequency Uncontrolled Rectifiers

Single Phase Line Frequency Uncontrolled Rectifiers Single Phae Line Frequency Unconrolle Recifier Kevin Gaughan 24-Nov-03 Single Phae Unconrolle Recifier 1 Topic Baic operaion an Waveform (nucive Loa) Power Facor Calculaion Supply curren Harmonic an Th

More information

CSE/NB 528 Lecture 14: From Supervised to Reinforcement Learning (Chapter 9) R. Rao, 528: Lecture 14

CSE/NB 528 Lecture 14: From Supervised to Reinforcement Learning (Chapter 9) R. Rao, 528: Lecture 14 CSE/NB 58 Lecure 14: From Supervised o Reinforcemen Learning Chaper 9 1 Recall from las ime: Sigmoid Neworks Oupu v T g w u g wiui w Inpu nodes u = u 1 u u 3 T i Sigmoid oupu funcion: 1 g a 1 a e 1 ga

More information

12. Nyquist Sampling, Pulse-Amplitude Modulation, and Time- Division Multiplexing

12. Nyquist Sampling, Pulse-Amplitude Modulation, and Time- Division Multiplexing Nyqui Sapling, Pule-Apliude Modulaion, and Tie Diviion Muliplexing on Mac 2. Nyqui Sapling, Pule-Apliude Modulaion, and Tie- Diviion Muliplexing Many analogue counicaion ye are ill in wide ue oday. Thee

More information

Lecture 15: Differential Pairs (Part 2)

Lecture 15: Differential Pairs (Part 2) Lecure 5: ifferenial Pairs (Par ) Gu-Yeon Wei ivision of Enineerin and Applied Sciences Harvard Universiy uyeon@eecs.harvard.edu Wei Overview eadin S&S: Chaper 6.6 Suppleenal eadin S&S: Chaper 6.9 azavi,

More information

( ) ( ) if t = t. It must satisfy the identity. So, bulkiness of the unit impulse (hyper)function is equal to 1. The defining characteristic is

( ) ( ) if t = t. It must satisfy the identity. So, bulkiness of the unit impulse (hyper)function is equal to 1. The defining characteristic is UNIT IMPULSE RESPONSE, UNIT STEP RESPONSE, STABILITY. Uni impulse funcion (Dirac dela funcion, dela funcion) rigorously defined is no sricly a funcion, bu disribuion (or measure), precise reamen requires

More information

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow.

Problem Set If all directed edges in a network have distinct capacities, then there is a unique maximum flow. CSE 202: Deign and Analyi of Algorihm Winer 2013 Problem Se 3 Inrucor: Kamalika Chaudhuri Due on: Tue. Feb 26, 2013 Inrucion For your proof, you may ue any lower bound, algorihm or daa rucure from he ex

More information

R.#W.#Erickson# Department#of#Electrical,#Computer,#and#Energy#Engineering# University#of#Colorado,#Boulder#

R.#W.#Erickson# Department#of#Electrical,#Computer,#and#Energy#Engineering# University#of#Colorado,#Boulder# .#W.#Erickson# Deparmen#of#Elecrical,#Compuer,#and#Energy#Engineering# Universiy#of#Colorado,#Boulder# Chaper 2 Principles of Seady-Sae Converer Analysis 2.1. Inroducion 2.2. Inducor vol-second balance,

More information

CHAPTER 7: SECOND-ORDER CIRCUITS

CHAPTER 7: SECOND-ORDER CIRCUITS EEE5: CI RCUI T THEORY CHAPTER 7: SECOND-ORDER CIRCUITS 7. Inroducion Thi chaper conider circui wih wo orage elemen. Known a econd-order circui becaue heir repone are decribed by differenial equaion ha

More information

Dimitri Solomatine. D.P. Solomatine. Data-driven modelling (part 2). 2

Dimitri Solomatine. D.P. Solomatine. Data-driven modelling (part 2). 2 Daa-driven modelling. Par. Daa-driven Arificial di Neural modelling. Newors Par Dimiri Solomaine Arificial neural newors D.P. Solomaine. Daa-driven modelling par. 1 Arificial neural newors ANN: main pes

More information

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff

Laplace transfom: t-translation rule , Haynes Miller and Jeremy Orloff Laplace ransfom: -ranslaion rule 8.03, Haynes Miller and Jeremy Orloff Inroducory example Consider he sysem ẋ + 3x = f(, where f is he inpu and x he response. We know is uni impulse response is 0 for

More information

Selfish Routing. Tim Roughgarden Cornell University. Includes joint work with Éva Tardos

Selfish Routing. Tim Roughgarden Cornell University. Includes joint work with Éva Tardos Selfih Rouing Tim Roughgarden Cornell Univeriy Include join work wih Éva Tardo 1 Which roue would you chooe? Example: one uni of raffic (e.g., car) wan o go from o delay = 1 hour (no congeion effec) long

More information

Designing Information Devices and Systems I Spring 2019 Lecture Notes Note 17

Designing Information Devices and Systems I Spring 2019 Lecture Notes Note 17 EES 16A Designing Informaion Devices and Sysems I Spring 019 Lecure Noes Noe 17 17.1 apaciive ouchscreen In he las noe, we saw ha a capacior consiss of wo pieces on conducive maerial separaed by a nonconducive

More information

LabQuest 24. Capacitors

LabQuest 24. Capacitors Capaciors LabQues 24 The charge q on a capacior s plae is proporional o he poenial difference V across he capacior. We express his wih q V = C where C is a proporionaliy consan known as he capaciance.

More information

Introduction to Congestion Games

Introduction to Congestion Games Algorihmic Game Theory, Summer 2017 Inroducion o Congeion Game Lecure 1 (5 page) Inrucor: Thoma Keelheim In hi lecure, we ge o know congeion game, which will be our running example for many concep in game

More information

Lectures 29 and 30 BIQUADRATICS AND STATE SPACE OP AMP REALIZATIONS. I. Introduction

Lectures 29 and 30 BIQUADRATICS AND STATE SPACE OP AMP REALIZATIONS. I. Introduction EE-202/445, 3/18/18 9-1 R. A. DeCarlo Lecures 29 and 30 BIQUADRATICS AND STATE SPACE OP AMP REALIZATIONS I. Inroducion 1. The biquadraic ransfer funcion has boh a 2nd order numeraor and a 2nd order denominaor:

More information

Linear Circuit Elements

Linear Circuit Elements 1/25/2011 inear ircui Elemens.doc 1/6 inear ircui Elemens Mos microwave devices can be described or modeled in erms of he hree sandard circui elemens: 1. ESISTANE () 2. INDUTANE () 3. APAITANE () For he

More information

Lecture 28: Single Stage Frequency response. Context

Lecture 28: Single Stage Frequency response. Context Lecure 28: Single Sage Frequency response Prof J. S. Sih Conex In oday s lecure, we will coninue o look a he frequency response of single sage aplifiers, saring wih a ore coplee discussion of he CS aplifier,

More information

Physics 240: Worksheet 16 Name

Physics 240: Worksheet 16 Name Phyic 4: Workhee 16 Nae Non-unifor circular oion Each of hee proble involve non-unifor circular oion wih a conan α. (1) Obain each of he equaion of oion for non-unifor circular oion under a conan acceleraion,

More information

13.1 Circuit Elements in the s Domain Circuit Analysis in the s Domain The Transfer Function and Natural Response 13.

13.1 Circuit Elements in the s Domain Circuit Analysis in the s Domain The Transfer Function and Natural Response 13. Chaper 3 The Laplace Tranform in Circui Analyi 3. Circui Elemen in he Domain 3.-3 Circui Analyi in he Domain 3.4-5 The Tranfer Funcion and Naural Repone 3.6 The Tranfer Funcion and he Convoluion Inegral

More information

EE202 Circuit Theory II

EE202 Circuit Theory II EE202 Circui Theory II 2017-2018, Spring Dr. Yılmaz KALKAN I. Inroducion & eview of Fir Order Circui (Chaper 7 of Nilon - 3 Hr. Inroducion, C and L Circui, Naural and Sep epone of Serie and Parallel L/C

More information

Reading. Lecture 28: Single Stage Frequency response. Lecture Outline. Context

Reading. Lecture 28: Single Stage Frequency response. Lecture Outline. Context Reading Lecure 28: Single Sage Frequency response Prof J. S. Sih Reading: We are discussing he frequency response of single sage aplifiers, which isn reaed in he ex unil afer uli-sae aplifiers (beginning

More information

Chapter 7: Solving Trig Equations

Chapter 7: Solving Trig Equations Haberman MTH Secion I: The Trigonomeric Funcions Chaper 7: Solving Trig Equaions Le s sar by solving a couple of equaions ha involve he sine funcion EXAMPLE a: Solve he equaion sin( ) The inverse funcions

More information

Reading from Young & Freedman: For this topic, read sections 25.4 & 25.5, the introduction to chapter 26 and sections 26.1 to 26.2 & 26.4.

Reading from Young & Freedman: For this topic, read sections 25.4 & 25.5, the introduction to chapter 26 and sections 26.1 to 26.2 & 26.4. PHY1 Elecriciy Topic 7 (Lecures 1 & 11) Elecric Circuis n his opic, we will cover: 1) Elecromoive Force (EMF) ) Series and parallel resisor combinaions 3) Kirchhoff s rules for circuis 4) Time dependence

More information

6.003 Homework #9 Solutions

6.003 Homework #9 Solutions 6.003 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 3 0 a 0 5 a k a k 0 πk j3 e 0 e j πk 0 jπk πk e 0

More information

6.8 Laplace Transform: General Formulas

6.8 Laplace Transform: General Formulas 48 HAP. 6 Laplace Tranform 6.8 Laplace Tranform: General Formula Formula Name, ommen Sec. F() l{ f ()} e f () d f () l {F()} Definiion of Tranform Invere Tranform 6. l{af () bg()} al{f ()} bl{g()} Lineariy

More information

The field of mathematics has made tremendous impact on the study of

The field of mathematics has made tremendous impact on the study of A Populaion Firing Rae Model of Reverberaory Aciviy in Neuronal Neworks Zofia Koscielniak Carnegie Mellon Universiy Menor: Dr. G. Bard Ermenrou Universiy of Pisburgh Inroducion: The field of mahemaics

More information

INDEX. Transient analysis 1 Initial Conditions 1

INDEX. Transient analysis 1 Initial Conditions 1 INDEX Secion Page Transien analysis 1 Iniial Condiions 1 Please inform me of your opinion of he relaive emphasis of he review maerial by simply making commens on his page and sending i o me a: Frank Mera

More information

Interpolation and Pulse Shaping

Interpolation and Pulse Shaping EE345S Real-Time Digial Signal Proceing Lab Spring 2006 Inerpolaion and Pule Shaping Prof. Brian L. Evan Dep. of Elecrical and Compuer Engineering The Univeriy of Texa a Auin Lecure 7 Dicree-o-Coninuou

More information

Slide03 Historical Overview Haykin Chapter 3 (Chap 1, 3, 3rd Ed): Single-Layer Perceptrons Multiple Faces of a Single Neuron Part I: Adaptive Filter

Slide03 Historical Overview Haykin Chapter 3 (Chap 1, 3, 3rd Ed): Single-Layer Perceptrons Multiple Faces of a Single Neuron Part I: Adaptive Filter Slide3 Haykin Chaper 3 (Chap, 3, 3rd Ed): Single-Layer Perceprons CPSC 636-6 Insrucor: Yoonsuck Choe Hisorical Overview McCulloch and Pis (943): neural neworks as compuing machines. Hebb (949): posulaed

More information

2.4 Cuk converter example

2.4 Cuk converter example 2.4 Cuk converer example C 1 Cuk converer, wih ideal swich i 1 i v 1 2 1 2 C 2 v 2 Cuk converer: pracical realizaion using MOSFET and diode C 1 i 1 i v 1 2 Q 1 D 1 C 2 v 2 28 Analysis sraegy This converer

More information

Chapter 13 Homework Answers

Chapter 13 Homework Answers Chaper 3 Homework Answers 3.. The answer is c, doubling he [C] o while keeping he [A] o and [B] o consan. 3.2. a. Since he graph is no linear, here is no way o deermine he reacion order by inspecion. A

More information

Chapter 1 Fundamental Concepts

Chapter 1 Fundamental Concepts Chaper 1 Fundamenal Conceps 1 Signals A signal is a paern of variaion of a physical quaniy, ofen as a funcion of ime (bu also space, disance, posiion, ec). These quaniies are usually he independen variables

More information

EECS 2602 Winter Laboratory 3 Fourier series, Fourier transform and Bode Plots in MATLAB

EECS 2602 Winter Laboratory 3 Fourier series, Fourier transform and Bode Plots in MATLAB EECS 6 Winer 7 Laboraory 3 Fourier series, Fourier ransform and Bode Plos in MATLAB Inroducion: The objecives of his lab are o use MATLAB:. To plo periodic signals wih Fourier series represenaion. To obain

More information

6.003 Homework #9 Solutions

6.003 Homework #9 Solutions 6.00 Homework #9 Soluions Problems. Fourier varieies a. Deermine he Fourier series coefficiens of he following signal, which is periodic in 0. x () 0 0 a 0 5 a k sin πk 5 sin πk 5 πk for k 0 a k 0 πk j

More information

Classical Conditioning IV: TD learning in the brain

Classical Conditioning IV: TD learning in the brain Classical Condiioning IV: TD learning in he brain PSY/NEU338: Animal learning and decision making: Psychological, compuaional and neural perspecives recap: Marr s levels of analysis David Marr (1945-1980)

More information

Bayesian Designs for Michaelis-Menten kinetics

Bayesian Designs for Michaelis-Menten kinetics Bayeian Deign for ichaeli-enen kineic John ahew and Gilly Allcock Deparen of Saiic Univeriy of Newcale upon Tyne.n..ahew@ncl.ac.uk Reference ec. on hp://www.a.ncl.ac.uk/~nn/alk/ile.h Enzyology any biocheical

More information

RC, RL and RLC circuits

RC, RL and RLC circuits Name Dae Time o Complee h m Parner Course/ Secion / Grade RC, RL and RLC circuis Inroducion In his experimen we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors.

More information

CSE/NB 528 Lecture 14: Reinforcement Learning (Chapter 9)

CSE/NB 528 Lecture 14: Reinforcement Learning (Chapter 9) CSE/NB 528 Lecure 14: Reinforcemen Learning Chaper 9 Image from hp://clasdean.la.asu.edu/news/images/ubep2001/neuron3.jpg Lecure figures are from Dayan & Abbo s book hp://people.brandeis.edu/~abbo/book/index.hml

More information

Lecture 13 RC/RL Circuits, Time Dependent Op Amp Circuits

Lecture 13 RC/RL Circuits, Time Dependent Op Amp Circuits Lecure 13 RC/RL Circuis, Time Dependen Op Amp Circuis RL Circuis The seps involved in solving simple circuis conaining dc sources, resisances, and one energy-sorage elemen (inducance or capaciance) are:

More information

1 Widrow-Hoff Algorithm

1 Widrow-Hoff Algorithm COS 511: heoreical Machine Learning Lecurer: Rob Schapire Lecure # 18 Scribe: Shaoqing Yang April 10, 014 1 Widrow-Hoff Algorih Firs le s review he Widrow-Hoff algorih ha was covered fro las lecure: Algorih

More information

Diebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles

Diebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles Diebold, Chaper 7 Francis X. Diebold, Elemens of Forecasing, 4h Ediion (Mason, Ohio: Cengage Learning, 006). Chaper 7. Characerizing Cycles Afer compleing his reading you should be able o: Define covariance

More information

Linear Algebra Primer

Linear Algebra Primer Linear Algebra rimer And a video dicuion of linear algebra from EE263 i here (lecure 3 and 4): hp://ee.anford.edu/coure/ee263 lide from Sanford CS3 Ouline Vecor and marice Baic Mari Operaion Deerminan,

More information

Direct Current Circuits. February 19, 2014 Physics for Scientists & Engineers 2, Chapter 26 1

Direct Current Circuits. February 19, 2014 Physics for Scientists & Engineers 2, Chapter 26 1 Direc Curren Circuis February 19, 2014 Physics for Scieniss & Engineers 2, Chaper 26 1 Ammeers and Volmeers! A device used o measure curren is called an ammeer! A device used o measure poenial difference

More information

CHAPTER 12 DIRECT CURRENT CIRCUITS

CHAPTER 12 DIRECT CURRENT CIRCUITS CHAPTER 12 DIRECT CURRENT CIUITS DIRECT CURRENT CIUITS 257 12.1 RESISTORS IN SERIES AND IN PARALLEL When wo resisors are conneced ogeher as shown in Figure 12.1 we said ha hey are conneced in series. As

More information

The Rosenblatt s LMS algorithm for Perceptron (1958) is built around a linear neuron (a neuron with a linear

The Rosenblatt s LMS algorithm for Perceptron (1958) is built around a linear neuron (a neuron with a linear In The name of God Lecure4: Percepron and AALIE r. Majid MjidGhoshunih Inroducion The Rosenbla s LMS algorihm for Percepron 958 is buil around a linear neuron a neuron ih a linear acivaion funcion. Hoever,

More information

10. State Space Methods

10. State Space Methods . Sae Space Mehods. Inroducion Sae space modelling was briefly inroduced in chaper. Here more coverage is provided of sae space mehods before some of heir uses in conrol sysem design are covered in he

More information

Lecture 1 Overview. course mechanics. outline & topics. what is a linear dynamical system? why study linear systems? some examples

Lecture 1 Overview. course mechanics. outline & topics. what is a linear dynamical system? why study linear systems? some examples EE263 Auumn 27-8 Sephen Boyd Lecure 1 Overview course mechanics ouline & opics wha is a linear dynamical sysem? why sudy linear sysems? some examples 1 1 Course mechanics all class info, lecures, homeworks,

More information

The average rate of change between two points on a function is d t

The average rate of change between two points on a function is d t SM Dae: Secion: Objecive: The average rae of change beween wo poins on a funcion is d. For example, if he funcion ( ) represens he disance in miles ha a car has raveled afer hours, hen finding he slope

More information

Chapter 6. Laplace Transforms

Chapter 6. Laplace Transforms 6- Chaper 6. Laplace Tranform 6.4 Shor Impule. Dirac Dela Funcion. Parial Fracion 6.5 Convoluion. Inegral Equaion 6.6 Differeniaion and Inegraion of Tranform 6.7 Syem of ODE 6.4 Shor Impule. Dirac Dela

More information

How to Solve System Dynamic s Problems

How to Solve System Dynamic s Problems How o Solve Sye Dynaic Proble A ye dynaic proble involve wo or ore bodie (objec) under he influence of everal exernal force. The objec ay uliaely re, ove wih conan velociy, conan acceleraion or oe cobinaion

More information

Ensamble methods: Boosting

Ensamble methods: Boosting Lecure 21 Ensamble mehods: Boosing Milos Hauskrech milos@cs.pi.edu 5329 Senno Square Schedule Final exam: April 18: 1:00-2:15pm, in-class Term projecs April 23 & April 25: a 1:00-2:30pm in CS seminar room

More information

Lab 10: RC, RL, and RLC Circuits

Lab 10: RC, RL, and RLC Circuits Lab 10: RC, RL, and RLC Circuis In his experimen, we will invesigae he behavior of circuis conaining combinaions of resisors, capaciors, and inducors. We will sudy he way volages and currens change in

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signals & Sysems Prof. Mar Fowler Noe Se #1 C-T Signals: Circuis wih Periodic Sources 1/1 Solving Circuis wih Periodic Sources FS maes i easy o find he response of an RLC circui o a periodic source!

More information

Chapter 2: Principles of steady-state converter analysis

Chapter 2: Principles of steady-state converter analysis Chaper 2 Principles of Seady-Sae Converer Analysis 2.1. Inroducion 2.2. Inducor vol-second balance, capacior charge balance, and he small ripple approximaion 2.3. Boos converer example 2.4. Cuk converer

More information

Today: Max Flow Proofs

Today: Max Flow Proofs Today: Max Flow Proof COSC 58, Algorihm March 4, 04 Many of hee lide are adaped from everal online ource Reading Aignmen Today cla: Chaper 6 Reading aignmen for nex cla: Chaper 7 (Amorized analyi) In-Cla

More information

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.

Nature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time. Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike

More information

6.302 Feedback Systems Recitation : Phase-locked Loops Prof. Joel L. Dawson

6.302 Feedback Systems Recitation : Phase-locked Loops Prof. Joel L. Dawson 6.32 Feedback Syem Phae-locked loop are a foundaional building block for analog circui deign, paricularly for communicaion circui. They provide a good example yem for hi cla becaue hey are an excellen

More information

More on ODEs by Laplace Transforms October 30, 2017

More on ODEs by Laplace Transforms October 30, 2017 More on OE b Laplace Tranfor Ocober, 7 More on Ordinar ifferenial Equaion wih Laplace Tranfor Larr areo Mechanical Engineering 5 Seinar in Engineering nali Ocober, 7 Ouline Review la cla efiniion of Laplace

More information

Chapter 8 The Complete Response of RL and RC Circuits

Chapter 8 The Complete Response of RL and RC Circuits Chaper 8 The Complee Response of RL and RC Circuis Seoul Naional Universiy Deparmen of Elecrical and Compuer Engineering Wha is Firs Order Circuis? Circuis ha conain only one inducor or only one capacior

More information

Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials: supplementary note

Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials: supplementary note Synapses wih shor-erm plasiciy are opimal esimaors of presynapic membrane poenials: supplemenary noe Jean-Pascal Pfiser, Peer Dayan, Máé Lengyel Supplemenary Noe 1 The local possynapic poenial In he main

More information

Graphs III - Network Flow

Graphs III - Network Flow Graph III - Nework Flow Flow nework eup graph G=(V,E) edge capaciy w(u,v) 0 - if edge doe no exi, hen w(u,v)=0 pecial verice: ource verex ; ink verex - no edge ino and no edge ou of Aume every verex v

More information

CHAPTER 10 VALIDATION OF TEST WITH ARTIFICAL NEURAL NETWORK

CHAPTER 10 VALIDATION OF TEST WITH ARTIFICAL NEURAL NETWORK 175 CHAPTER 10 VALIDATION OF TEST WITH ARTIFICAL NEURAL NETWORK 10.1 INTRODUCTION Amongs he research work performed, he bes resuls of experimenal work are validaed wih Arificial Neural Nework. From he

More information

Chapter 6. Laplace Transforms

Chapter 6. Laplace Transforms Chaper 6. Laplace Tranform Kreyzig by YHLee;45; 6- An ODE i reduced o an algebraic problem by operaional calculu. The equaion i olved by algebraic manipulaion. The reul i ranformed back for he oluion of

More information

CHEMICAL KINETICS: 1. Rate Order Rate law Rate constant Half-life Temperature Dependence

CHEMICAL KINETICS: 1. Rate Order Rate law Rate constant Half-life Temperature Dependence CHEMICL KINETICS: Rae Order Rae law Rae consan Half-life Temperaure Dependence Chemical Reacions Kineics Chemical ineics is he sudy of ime dependence of he change in he concenraion of reacans and producs.

More information

EE 301 Lab 2 Convolution

EE 301 Lab 2 Convolution EE 301 Lab 2 Convoluion 1 Inroducion In his lab we will gain some more experience wih he convoluion inegral and creae a scrip ha shows he graphical mehod of convoluion. 2 Wha you will learn This lab will

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Linear Response Theory: The connecion beween QFT and experimens 3.1. Basic conceps and ideas Q: How do we measure he conduciviy of a meal? A: we firs inroduce a weak elecric field E, and

More information

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...

t is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t... Mah 228- Fri Mar 24 5.6 Marix exponenials and linear sysems: The analogy beween firs order sysems of linear differenial equaions (Chaper 5) and scalar linear differenial equaions (Chaper ) is much sronger

More information

Chapter 21. Reinforcement Learning. The Reinforcement Learning Agent

Chapter 21. Reinforcement Learning. The Reinforcement Learning Agent CSE 47 Chaper Reinforcemen Learning The Reinforcemen Learning Agen Agen Sae u Reward r Acion a Enironmen CSE AI Faculy Why reinforcemen learning Programming an agen o drie a car or fly a helicoper is ery

More information

CSE 3802 / ECE Numerical Methods in Scientific Computation. Jinbo Bi. Department of Computer Science & Engineering

CSE 3802 / ECE Numerical Methods in Scientific Computation. Jinbo Bi. Department of Computer Science & Engineering CSE 3802 / ECE 3431 Numerical Mehods in Scienific Compuaion Jinbo Bi Deparmen of Compuer Science & Engineering hp://www.engr.uconn.edu/~jinbo 1 Ph.D in Mahemaics The Insrucor Previous professional experience:

More information

Linear Motion, Speed & Velocity

Linear Motion, Speed & Velocity Add Iporan Linear Moion, Speed & Velociy Page: 136 Linear Moion, Speed & Velociy NGSS Sandard: N/A MA Curriculu Fraework (2006): 1.1, 1.2 AP Phyic 1 Learning Objecive: 3.A.1.1, 3.A.1.3 Knowledge/Underanding

More information

2.7. Some common engineering functions. Introduction. Prerequisites. Learning Outcomes

2.7. Some common engineering functions. Introduction. Prerequisites. Learning Outcomes Some common engineering funcions 2.7 Inroducion This secion provides a caalogue of some common funcions ofen used in Science and Engineering. These include polynomials, raional funcions, he modulus funcion

More information

8. Basic RL and RC Circuits

8. Basic RL and RC Circuits 8. Basic L and C Circuis This chaper deals wih he soluions of he responses of L and C circuis The analysis of C and L circuis leads o a linear differenial equaion This chaper covers he following opics

More information

6.003: Signals and Systems. Lecture 1 Introduction to Signals and Systems

6.003: Signals and Systems. Lecture 1 Introduction to Signals and Systems 6.003: Signals and Sysems Lecure 1 Inroducion o Signals and Sysems 6.003: Signals and Sysems Today s handous: Single package conaining Subjec Informaion Lecure #1 slides (for oday) Reciaion #2 handou (for

More information

Chapter 7 Response of First-order RL and RC Circuits

Chapter 7 Response of First-order RL and RC Circuits Chaper 7 Response of Firs-order RL and RC Circuis 7.- The Naural Response of RL and RC Circuis 7.3 The Sep Response of RL and RC Circuis 7.4 A General Soluion for Sep and Naural Responses 7.5 Sequenial

More information

k B 2 Radiofrequency pulses and hardware

k B 2 Radiofrequency pulses and hardware 1 Exra MR Problems DC Medical Imaging course April, 214 he problems below are harder, more ime-consuming, and inended for hose wih a more mahemaical background. hey are enirely opional, bu hopefully will

More information

Chapter 7: Inverse-Response Systems

Chapter 7: Inverse-Response Systems Chaper 7: Invere-Repone Syem Normal Syem Invere-Repone Syem Baic Sar ou in he wrong direcion End up in he original eady-ae gain value Two or more yem wih differen magniude and cale in parallel Main yem

More information

An recursive analytical technique to estimate time dependent physical parameters in the presence of noise processes

An recursive analytical technique to estimate time dependent physical parameters in the presence of noise processes WHAT IS A KALMAN FILTER An recursive analyical echnique o esimae ime dependen physical parameers in he presence of noise processes Example of a ime and frequency applicaion: Offse beween wo clocks PREDICTORS,

More information

EE 435. Lecture 31. Absolute and Relative Accuracy DAC Design. The String DAC

EE 435. Lecture 31. Absolute and Relative Accuracy DAC Design. The String DAC EE 435 Lecure 3 Absolue and Relaive Accuracy DAC Design The Sring DAC . Review from las lecure. DFT Simulaion from Malab Quanizaion Noise DACs and ADCs generally quanize boh ampliude and ime If convering

More information

MEMS 0031 Electric Circuits

MEMS 0031 Electric Circuits MEMS 0031 Elecric Circuis Chaper 1 Circui variables Chaper/Lecure Learning Objecives A he end of his lecure and chaper, you should able o: Represen he curren and volage of an elecric circui elemen, paying

More information

PHYSICS 151 Notes for Online Lecture #4

PHYSICS 151 Notes for Online Lecture #4 PHYSICS 5 Noe for Online Lecure #4 Acceleraion The ga pedal in a car i alo called an acceleraor becaue preing i allow you o change your elociy. Acceleraion i how fa he elociy change. So if you ar fro re

More information

The Residual Graph. 11 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm

The Residual Graph. 11 Augmenting Path Algorithms. Augmenting Path Algorithm. Augmenting Path Algorithm Augmening Pah Algorihm Greedy-algorihm: ar wih f (e) = everywhere find an - pah wih f (e) < c(e) on every edge augmen flow along he pah repea a long a poible The Reidual Graph From he graph G = (V, E,

More information

Chapter 9 Sinusoidal Steady State Analysis

Chapter 9 Sinusoidal Steady State Analysis Chaper 9 Sinusoidal Seady Sae Analysis 9.-9. The Sinusoidal Source and Response 9.3 The Phasor 9.4 pedances of Passive Eleens 9.5-9.9 Circui Analysis Techniques in he Frequency Doain 9.0-9. The Transforer

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 30 Signal & Syem Prof. ark Fowler oe Se #34 C-T Tranfer Funcion and Frequency Repone /4 Finding he Tranfer Funcion from Differenial Eq. Recall: we found a DT yem Tranfer Funcion Hz y aking he ZT of

More information

Announcements: Warm-up Exercise:

Announcements: Warm-up Exercise: Fri Apr 13 7.1 Sysems of differenial equaions - o model muli-componen sysems via comparmenal analysis hp//en.wikipedia.org/wiki/muli-comparmen_model Announcemens Warm-up Exercise Here's a relaively simple

More information

UNIVERSITY OF CALIFORNIA AT BERKELEY

UNIVERSITY OF CALIFORNIA AT BERKELEY Homework #10 Soluions EECS 40, Fall 2006 Prof. Chang-Hasnain Due a 6 pm in 240 Cory on Wednesday, 04/18/07 oal Poins: 100 Pu (1) your name and (2) discussion secion number on your homework. You need o

More information

Ensamble methods: Bagging and Boosting

Ensamble methods: Bagging and Boosting Lecure 21 Ensamble mehods: Bagging and Boosing Milos Hauskrech milos@cs.pi.edu 5329 Senno Square Ensemble mehods Mixure of expers Muliple base models (classifiers, regressors), each covers a differen par

More information

Randomized Perfect Bipartite Matching

Randomized Perfect Bipartite Matching Inenive Algorihm Lecure 24 Randomized Perfec Biparie Maching Lecurer: Daniel A. Spielman April 9, 208 24. Inroducion We explain a randomized algorihm by Ahih Goel, Michael Kapralov and Sanjeev Khanna for

More information

R =, C = 1, and f ( t ) = 1 for 1 second from t = 0 to t = 1. The initial charge on the capacitor is q (0) = 0. We have already solved this problem.

R =, C = 1, and f ( t ) = 1 for 1 second from t = 0 to t = 1. The initial charge on the capacitor is q (0) = 0. We have already solved this problem. Theoreical Physics Prof. Ruiz, UNC Asheville, docorphys on YouTube Chaper U Noes. Green's Funcions R, C 1, and f ( ) 1 for 1 second from o 1. The iniial charge on he capacior is q (). We have already solved

More information

EECS 141: FALL 00 MIDTERM 2

EECS 141: FALL 00 MIDTERM 2 Universiy of California College of Engineering Deparmen of Elecrical Engineering and Compuer Science J. M. Rabaey TuTh9:30-11am ee141@eecs EECS 141: FALL 00 MIDTERM 2 For all problems, you can assume he

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signals & Sysems Prof. Mark Fowler Noe Se #1 C-T Sysems: Convoluion Represenaion Reading Assignmen: Secion 2.6 of Kamen and Heck 1/11 Course Flow Diagram The arrows here show concepual flow beween

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER John Riley 6 December 200 NWER TO ODD NUMBERED EXERCIE IN CHPTER 7 ecion 7 Exercie 7-: m m uppoe ˆ, m=,, M (a For M = 2, i i eay o how ha I implie I From I, for any probabiliy vecor ( p, p 2, 2 2 ˆ ( p,

More information

Chaos-induced modulation of reliability boosts output firing rate in downstream cortical areas

Chaos-induced modulation of reliability boosts output firing rate in downstream cortical areas PHYSICAL REVIEW E 69, 031912 2004 Chaos-induced modulaion of reliabiliy booss oupu firing rae in downsream corical areas P. H. E. Tiesinga Deparmen of Physics & Asronomy, Universiy of Norh Carolina, Chapel

More information

8.022 (E&M) Lecture 9

8.022 (E&M) Lecture 9 8.0 (E&M) Lecure 9 Topics: circuis Thevenin s heorem Las ime Elecromoive force: How does a baery work and is inernal resisance How o solve simple circuis: Kirchhoff s firs rule: a any node, sum of he currens

More information

Age (x) nx lx. Age (x) nx lx dx qx

Age (x) nx lx. Age (x) nx lx dx qx Life Tables Dynamic (horizonal) cohor= cohor followed hrough ime unil all members have died Saic (verical or curren) = one census period (day, season, ec.); only equivalen o dynamic if populaion does no

More information

When analyzing an object s motion there are two factors to consider when attempting to bring it to rest. 1. The object s mass 2. The object s velocity

When analyzing an object s motion there are two factors to consider when attempting to bring it to rest. 1. The object s mass 2. The object s velocity SPH4U Momenum LoRuo Momenum i an exenion of Newon nd law. When analyzing an ojec moion here are wo facor o conider when aeming o ring i o re.. The ojec ma. The ojec velociy The greaer an ojec ma, he more

More information

LAPLACE TRANSFORM AND TRANSFER FUNCTION

LAPLACE TRANSFORM AND TRANSFER FUNCTION CHBE320 LECTURE V LAPLACE TRANSFORM AND TRANSFER FUNCTION Professor Dae Ryook Yang Spring 2018 Dep. of Chemical and Biological Engineering 5-1 Road Map of he Lecure V Laplace Transform and Transfer funcions

More information