Verfügbarkeit: State-dependent processing in spiking neural networks

State-dependent processing in spiking neural networks:

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Bibliographische Detailangaben
1. Verfasser:	Duarte, Renato (VerfasserIn)
Format:	Abschlussarbeit Buch
Sprache:	English
Veröffentlicht:	Freiburg im Breisgau [2018]
Schlagworte:	Hochschulschrift
Online-Zugang:	Inhaltsverzeichnis Inhaltsverzeichnis
Beschreibung:	Enthält Sonderabdrucke
Beschreibung:	xv, 214 Seiten Illustrationen, Diagramme 30 cm

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MARC


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Datensatz im Suchindex

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adam_text	CONTENTS FOREWORD 1 1 INTRODUCTION 3 1.1 THE NATURE OF THE PROBLEM ............................................................................ 4 1.1.1 PATTERN PERCEPTION AND RULE LE A R N IN G .......................................... 5 1.1.2 RECURRENT NETWORKS AS EXCITABLE RESERVOIRS................................. 6 1.2 BENCHMARKING N E U RO B IO LO G Y ..................................................................... 7 1.3 SCOPE AND S TRU C TU RE ..................................................................................... 9 2 DYNAMIC STABILITY OF SEQUENTIAL STIMULUS REPRESENTATIONS IN ADAPTING NEU RONAL NETWORKS 11 2.1 INTRODUCTION................................................................................................. 12 2.2 MATERIALS AND M E TH O D S ................................................... 14 2.2.1 N ETW ORK........................................................................................... 14 2.2.2 SYNAPTIC P LA S TIC ITY ....................................................................... 15 2.2.3 CONSTRAINING MODEL PARAMETERS ................................................. 15 2.2.4 INPUT S TIM U LI................................................................................. 16 2.2.5 DATA A N ALYSIS................................................................................. 16 2.2.6 NUMERICAL S IM ULATIO N S................................................................. 18 2.3 R ESULTS........................................................................................................... 19 2.3.1 IMPACT OF PLASTICITY ON NETWORK D Y N A M IC S ................................ 19 2.3.2 STIMULUS DISCRIMINATION .............................................................. 20 2.3.3 DIFFERENTIAL EFFECTS OF PLASTICITY ................................................. 25 2.4 D ISCUSSION.................................................................................................... 27 2.5 REFERENCES.................................................................................................... 29 3 EXPANSION AND STATE-DEPENDENT VARIABILITY ALONG SENSORY PROCESSING STREAMS 33 3.1 REFERENCES.................................................................................................... 35 4 CLOSED LOOP INTERACTIONS BETWEEN SPIKING NEURAL NETWORKS AND ROBOTIC SIM ULATORS BASED ON MUSIC AND ROS 37 4.1 INTRODUCTION................................................................................................. 38 4.1.1 DESCRIPTION OF THE TO O LC H A IN ....................................................... 39 4.2 MATERIALS AND M E TH O D S .............................................................................. 39 4.2.1 THE MUSIC CONFIGURATION F I L E .................................................... 39 4.2.2 ENCODING DECODING AND A D A P TA TIO N .......................................... 39 XII CONTENTS 4.2.3 PERFORMANCE MEASUREMENTS ........................................................ 41 4.3 R ESULTS........................................................................................................... 42 4.3.1 PERFORMANCE ................................................................................. 42 4.3.2 IMPLEMENTATION OF A BRAITENBERG V EH ICLE.................................... 44 4.4 D ISCUSSION..................................................................................................... 44 4.4.1 P E RFO RM A N C E ................................................................................. 44 4.4.2 COMPLEXITY OF NEURONAL SIM U LA TIO N S .......................................... 45 4.4.3 A P P LIC A TIO N S ................................................................................. 45 4.5 R EFERENCES..................................................................................................... 46 5 LIQUID COMPUTING ON AND OFF THE EDGE OF CHAOS WITH A STRIATAL MICROCIRCUIT 49 5.1 INTRODUCTION.................................................................................................. 50 5.2 R ESULTS........................................................................................................... 52 5.2.1 STRIATAL MICROCIRCUIT .................................................................... 52 5.2.2 ACTIVITY STATISTICIS OF STRIATAL MICROCIRCUIT M O D E L ....................... 53 5.2.3 SEPARATION P RO P ERTY ........................................................................ 54 5.2.4 COMPUTATIONAL PROPERTIES OF THE STRIATAL MICROCIRCUIT MODEL . . 55 5.3 D ISCUSSION..................................................................................................... 59 5.4 MATERIALS AND M E TH O D S .............................................................................. 62 5.4.1 INPUT E N C O D IN G .............................................................................. 62 5.4.2 ACTIVITY ANALYSIS METHODS ........................................................... 62 5.4.3 LINEAR RE A D O U TS .............................................................................. 63 5.4.4 COMPUTATIONAL PROPERTIES.............................................................. 63 5.5 R EFERENCES..................................................................................................... 64 6 THE BEST SPIKE FILTER KERNEL IS A NEURON 67 6.1 SPIKING NEURAL NETWORK M O D E LS ................................................................. 68 6.1.1 D E C O D IN G ........................................................................................ 69 6.1.2 CHOICE OF K E R N E L ............................................................................ 69 6.1.3 STOCK-FLOW D U A L I T Y ......................................................................... 69 6.1.4 DECODING ON STA TE -V A RIA B LE S ........................................................ 69 6.1.5 NEURONS AS SPIKE FILTERING K E R N E L S .............................................. 69 6.2 EXPERIMENTAL RESULTS ......................................................................... 69 6.3 CONCLUSION .................................................................................................. 69 6.4 R EFERENCES..................................................................................................... 69 7 SELF-ORGANIZED ARTIFICIAL GRAMMAR LEARNING IN SPIKING NEURAL NETWORKS 71 7.1 IN TRODUCTION.................................................................................................. 72 7.1.1 ARTIFICIAL GRAMMAR L E A RN IN G ....................................................... 72 7.1.2 COMPUTING WITH NEURAL CIRCU ITS .................................................... 73 7.2 SELF-ORGANIZING SPIKING NETWORK ........................................................... 73 7.2.1 A D A P TA TIO N ..................................................................................... 74 7.2.2 INPUT S TR U C TU RE .............................................................................. 74 7.3 PREDICTIVE MODELLING FOR THE R G .............................................................. 74 7.3.1 PREDICTION PERFORM ANCE................................................................. 75 7.4 JUDGING STRING L E G A LITY .............................................................................. 76 7.5 D ISCUSSION..................................................................................................... 77 7.6 R EFERENCES..................................................................................................... 77 8 SYNAPTIC PATTERNING AND THE TIMESCALES OF CORTICAL DYNAM ICS 79 8.1 INTRODUCTION................................................................................................. 80 8.2 FORM FOLLOWS FUNCTION .............................................................................. 81 8.2.1 CORTICAL TRANSCRIPTION P A TTE R N S .................................................... 81 8.2.2 RECEPTOR FINGERPRINTS IN THE ADULT C O R TE X ................ 81 8.3 TEMPORAL RECEPTIVITY IN CORTICAL CIRCUITS ................................................. 82 8.3.1 BALANCED STATES AND EMERGENT TEMPORAL S TR U C TU R E ..................... 83 8.3.2 RECURRENT ECHOES THROUGH COMPLEX SYNAPSES .......................... 84 8.3.3 MULTIPLE TIMESCALES OF ACTIVITY-DEPENDENT MODIFICATIONS . . . 85 8.4 CONCLUSIONS AND O U TLO O K ........................................................................... 85 9 LEVERAGING HETEROGENEITY FOR NEURAL COM PUTATION WITH FADING M EM ORY IN LAYER 2/3 CORTICAL M ICROCIRCUITS 91 9.1 INTRODUCTION................................................................................................. 94 9.1.1 HETEROGENEOUS BUILDING BLOCKS IN THE NECORTICAL CIRCUITRY . . . 94 9.1.2 DESCRIPTIVE ADEQU ACY .................................................................... 96 9.2 BUILDING THE M ICRO CIRCU IT.......................................................................... 97 9.2.1 NEURONAL PROPERTIES ....................................................................... 98 9.2.2 SYNAPTIC P RO P E RTIE S ....................................................................... 100 9.2.3 STRUCTURAL P RO P E RTIE S .................................................................... 102 9.3 EMERGENT POPULATION D Y N AM ICS................................................................. 103 9.3.1 EXCITATION/INHIBITION B A LA N C E ....................................................... 104 9.4 ACTIVE PROCESSING AND COM PUTATION .......................................................... 106 9.4.1 SPIKING ACTIVITY IN THE ACTIVE S T A T E ............................................. 106 9.4.2 TEMPORAL TUNING AND MEMORY CAPACITY ....................................... 108 9.4.3 PROCESSING CAPACITY ....................................................................... I L L 9.5 D ISCUSSION.................................................................................................... 112 9.5.1 CORTICAL STA TE S................................................................................. 113 9.5.2 HETEROGENEITY AND INFORMATION PROCESSING ................................ 114 9.5.3 LIMITATIONS AND FUTURE W O R K ....................................................... 115 9.5.4 DATA-DRIVEN COMPUTATIONAL NEUROSCIENCE.................................... 116 9.6 MATERIALS AND M E TH O D S .............................................................................. 117 9.6.1 NEURONAL D Y N AM ICS ....................................................................... 117 9.6.2 SYNAPTIC D Y N A M IC S ....................................................................... 117 9.6.3 GENERATING STRUCTURAL H E TE RO G E N E ITY .......................................... 117 9.6.4 PROFILING THE M IC RO C IRC U ITS .......................................................... 118 9.6.5 NUMERICAL SIMULATIONS IMPLEMENTATION AND DATA ANALYSIS . . . 120 9.7 REFERENCES.................................................................................................... 121 10 DISCUSSION AND OUTLOOK 139 10.1 FUNCTIONAL N E U RO D Y N A M IC S........................................................................ 140 10.1.1 STATE-DEPENDENCE AND PROCESSING P R E C IS IO N ............................. 141 10.1.2 INTRINSIC MANIFOLDS AND STRUCTURE LE A R N IN G .................................. 143 10.2 FUNCTIONAL M A P P IN G S .................................................................................. 145 10.2.1 INPUT TRANSDUCTION - THE ENCODING P RO B LE M ................................. 146 10.2.2 STATE TRANSDUCTION - THE DECODING PRO B LE M .................................. 148 10.3 PROCESSING M E M O RY ..................................................................................... 148 10.4 COMPOSITIONAL HIERARCHIES ........................................................................ 150 10.4.1 HIERARCHICAL PROCESSING................................................................. 152 APPENDICES 153 A NEURAL MICROCIRCUIT SIMULATION AND ANALYSIS TOOLKIT (NMSAT) 155 A .L D ISCLAIM ER.................................................................................................... 156 A.2 GETTING S T A R T E D .......................................................................................... 156 A.2.1 D EPENDENCIES................................................................................. 156 A.2.2 IN S TA LLIN G ........................................................................................ 156 A.3 RUNNING AN E X P E RIM E N T............................................................................. 157 A.4 SIMULATION O U TP U T....................................................................................... 157 A.5 ANALYSING AND P L O T T I N G ............................................................................. 157 A.6 HARVESTING STORED R E S U L T S .......................................................................... 158 A.7 E X A M P LE S .................................................................................................... 158 A.8 AUTHORS AND C O N TRIB U TO RS .......................................................................... 158 A.9 HELP AND S U P P O R T ....................................................................................... 158 A. 10 CITING U S ....................................................................................................... 158 A . L 1 L I C E N S E ....................................................................................................... 158 B SUPPLEMENTARY MATERIALS FOR CHAPTER 2 161 B . L SUPPLEMENTARY F IG U R E S ............................................................................. 163 B.2 SUPPLEMENTARY T ABLES................................................................................ 167 B. 3 R EFERENCES.................................................................................................... 170 C SUPPLEMENTARY MATERIALS FOR CHAPTER 5 171 C . L SUPLEMENTARY T A B L E S ................................................................................ 172 C. 2 R EFERENCES............................................................................ 176 D SUPPLEMENTARY MATERIALS FOR CHAPTER 9 177 D . L SUPLEMENTARY T A B L E S ................................................................................ 179 D.2 PRIMARY DATA S O U RC E S................................................................................ 181 D.3 SUPPLEMENTARY F IG U R E S ............................................................................. 183 D.4 REPRODUCIBILITY AND R E P LIC A TIO N ............................................................ 187 D.4.1 OSF P RO JE C T.................................................................................... 187 D.4.2 SOFTWARE AND SOURCE C O D E .......................................................... 187 D.4.3 D A TASE TS........................................................................................... 188 D.5 REFERENCES 191
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spelling	Duarte, Renato Verfasser (DE-588)1160899533 aut State-dependent processing in spiking neural networks presented by Renato Duarte Freiburg im Breisgau [2018] xv, 214 Seiten Illustrationen, Diagramme 30 cm txt rdacontent n rdamedia nc rdacarrier Enthält Sonderabdrucke Dissertation Albert-Ludwigs-Universität Freiburg im Breisgau 2018 (DE-588)4113937-9 Hochschulschrift gnd-content B:DE-101 application/pdf http://d-nb.info/1163755281/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030618149&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Duarte, Renato State-dependent processing in spiking neural networks
subject_GND	(DE-588)4113937-9
title	State-dependent processing in spiking neural networks
title_auth	State-dependent processing in spiking neural networks
title_exact_search	State-dependent processing in spiking neural networks
title_full	State-dependent processing in spiking neural networks presented by Renato Duarte
title_fullStr	State-dependent processing in spiking neural networks presented by Renato Duarte
title_full_unstemmed	State-dependent processing in spiking neural networks presented by Renato Duarte
title_short	State-dependent processing in spiking neural networks
title_sort	state dependent processing in spiking neural networks
topic_facet	Hochschulschrift
url	http://d-nb.info/1163755281/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030618149&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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