Time-space, spiking neural networks and brain-inspired artificial intelligence:
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[2019]
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| Schriftenreihe: | Springer series on bio- and neurosystems
Volume 7 |
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| Beschreibung: | xxxiv, 738 Seiten Illustrationen, Diagramme |
| ISBN: | 9783662577134 |
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Datensatz im Suchindex
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CONTENTS
PART I TIME-SPACE AND AI. ARTIFICIAL NEURAL NETWORKS
1 EVOLVING PROCESSES IN TIME-SPACE. DEEP LEARNING AND DEEP
KNOWLEDGE REPRESENTATION IN TIME-SPACE. BRAIN-INSPIRED AI . . . . 3
1.1 EVOLVING PROCESSES IN
TIME-SPACE. 3
1.1.1 WHAT ARE EVOLVING PROCESSES?
.
4
1.1.2 EVOLVING PROCESSES IN LIVING ORGANISMS. 5
1.1.3 SPATIO-TEMPORAL AND SPECTRO-TEMPORAL EVOLVING
PROCESSES.
8
1.2 CHARACTERISTICS OF EVOLVING PROCESSES: FREQUENCY, ENERGY,
PROBABILITY, ENTROPY AND
INFORMATION. 9
1.3 LIGHT AND SOUND
.
15
1.4 EVOLVING PROCESSES IN TIME-SPACE AND D
IRECTION. 18
1.5 FROM DATA AND INFORMATION TO KNOWLEDGE
.
19
1.6 DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION
IN TIME-SPACE. HOW D EEP?
.
22
1.6.1 DEFINING DEEP KNOWLEDGE IN TIM E-SPACE
.
22
1.6.2 HOW D E E P ?
.
25
1.6.3 EXAMPLES OF DEEP KNOWLEDGE REPRESENTATION
IN THIS B O O K
.
26
1.7 STATISTICAL, COMPUTATIONAL MODELLING OF EVOLVING PROCESSES . . . 26
1.7.1 STATISTICAL METHODS FOR COMPUTATIONAL M ODELLING
.
27
1.7.2 GLOBAL, LOCAL AND TRANSDUCTIVE (*PERSONALISED*)
M
ODELLING.
28
1.7.3 MODEL V
ALIDATION.
31
1.8 BRAIN-INSPIRED A I
.
32
1.9 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
35
R
EFERENCES.
36
2 ARTIFICIAL NEURAL NETWORKS. EVOLVING CONNECTIONIST
SYSTEM
S.
39
2.1 CLASSICAL ARTIFICIAL NEURAL NETWORKS: SOM, MLP,
CNN, R N N
.
39
2.1.1 UNSUPERVISED LEARNING IN NEURAL NETWORKS.
SELF-ORGANISING MAPS (SOM).
40
2.1.2 SUPERVISED LEARNING IN ANN. MULTILAYER
PERCEPTRON AND THE BACK PROPAGATION A LG O RITH M
.
44
2.1.3 CONVOLUTIONAL NEURAL NETWORKS (C N N )
.
48
2.1.4 RECURRENT AND LSTM A N N
.
49
2.2 HYBRID AND KNOWLEDGE-BASED A N N
. 50
2.3 EVOLVING CONNECTIONIST SYSTEMS (EC O
S). 52
2.3.1 PRINCIPLES OF E C O S
. 52
2.3.2 EVOLVING SELF-ORGANISING M A P S
.
53
2.3.3 EVOLVING M L P
.
56
2.4 EVOLVING FUZZY NEURAL NETWORKS. EFUN N
.
60
2.5 DYNAMIC EVOLVING NEURO-FUZZY INFERENCE
SYSTEMS*D EN FIS
.
70
2.6 OTHER ECOS METHODS AND S YSTEM S
.
75
2.7 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE
.
77
REFERENCES.
78
PART II THE HUMAN BRAIN
3 DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION
IN THE HUMAN B R A IN
.
87
3.1 TIME-SPACE IN THE B RA IN
. 87
3.2 LEARNING AND
MEMORY.
93
3.3 NEURAL REPRESENTATION OF
INFORMATION. 95
3.4 PERCEPTION IN THE BRAIN IS ALWAYS SPATIO/SPECTRO-TEMPORAL . 97
3.5 DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION
IN TIME-SPACE IN THE B RA IN
.
103
3.6 INFORMATION AND SIGNAL PROCESSING IN NEURONS
AND IN THE B RA IN
.
107
3.6.1 INFORMATION C O D IN G
. 107
3.6.2 MOLECULAR BASIS OF INFORMATION P ROCESSING
.
109
3.7 MEASURING BRAIN ACTIVITIES AS SPATIO/SPECTRO-TEMPORAL
D
ATA.
I L L
3.7.1 GENERAL N
OTIONS.
I L L
3.7.2 ELECTROENCEPHALOGRAM (EEG) D A T A
.
113
3.7.3 M E G
.
116
3.7.4 CT AND PET
.
116
3.7.5 FM R
I.
117
3.8 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
119
REFERENCES.
120
P ART I N SPIKING NEURAL NETWORKS
4 METHODS OF SPIKING NEURAL N ETW O RK
S. 127
4.1 INFORMATION REPRESENTATION AS SPIKES. SPIKE ENCODING
ALGORITHMS.
127
4.1.1 RATE VERSUS SPIKE TIME INFORMATION
REPRESENTATION
.
127
4.1.2 SPIKE ENCODING A LGORITHM
S. 129
4.2 SPIKING NEURON M O D E LS
. 137
4.2.1 HODGKIN-HUXLEY MODEL (H H M
). 137
4.2.2 LEAKY INTEGRATE-AND-FIRE MODEL (L IF M ).
138
4.2.3 IZHIKEVICH MODEL (IM )
.
140
4.2.4 SPIKE RESPONSE MODEL (S R M )
.
140
4.2.5 THORPE*S MODEL (TM
). 142
4.2.6 PROBABILISTIC AND STOCHASTIC SPIKING
NEURON M O D E LS
. 142
4.2.7 PROBABILISTIC NEUROGENETIC MODEL OF A N EURON
.
143
4.3 METHODS FOR LEARNING IN S N N
. 145
4.3.1 S PIKEPROP
.
146
4.3.2 SPIKE-TIME DEPENDENT PLASTICITY (STOP). 147
4.3.3 SPIKE-DRIVEN SYNAPTIC PLASTICITY (S D S P).
149
4.3.4 RANK ORDER (RO) LEARNING R U LE
. 149
4.3.5 LEARNING IN DYNAMIC SYNAPSES
.
150
4.4 SPIKE PATTERN ASSOCIATION NEURONS AND NEURAL N ETW O RK S
.
151
4.4.1 PRINCIPLES OF SPIKE PATTERN ASSOCIATION LEARNING.
THE SPAN MODEL
.
151
4.4.2 CASE STUDY EXAM
PLES. 155
4.4.3 MEMORY CAPACITY OF S P A N
. 158
4.4.4 SPAN FOR CLASSIFICATION PROBLEM S
.
160
4.5 WHY USE S N N ?
.
162
4.6 SUMMARY AND FURTHER READINGS FOR DEEPER K NOW LEDGE
.
163
REFERENCES.
164
5 EVOLVING SPIKING NEURAL N ETW
ORKS. 169
5.1 PRINCIPLES AND METHODS OF EVOLVING SNN (ESN N )
.
169
5.2 CONVOLUTIONAL ESNN (C ESN N )
.
175
5.3 DYNAMIC EVOLVING SNN
(DESNN). 179
5.4 FUZZY RULE EXTRACTION FROM E S N N
.
183
5.4.1 FUZZY RULE EXTRACTION FROM E S N N
.
183
5.4.2 A CASE STUDY OF FUZZY RULE EXTRACTION
FROM WATER TASTANT SENSORY D A TA
.
188
5.5 EVOLVING SNN FOR RESERVOIR C OM
PUTING. 193
5.5.1 RESERVOIR ARCHITECTURES. LIQUID STATE
MACHINES (L S M
). 193
5.5.2 ESNN/DESNN AS CLASSIFICATION/REGRESSION
SYSTEMS FOR RESERVOIR A RCHITECTURES. 195
5.6 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
197
REFERENCES.
198
6 BRAIN-INSPIRED SNN FOR DEEP LEARNING IN TIME-SPACE AND DEEP
KNOWLEDGE REPRESENTATION. N EUC
UBE. 201
6.1 BRAIN INSPIRED SNN (BI-SNN). THE BI-SNN NEUCUBE
AS A GENERIC SPATIO-TEMPORAL DATA M ACHINE
.
201
6.1.1 A GENERAL ARCHITECTURE OF A BI-SNN.
201
6.1.2 THE BI-SNN NEUCUBE AS A GENERIC SPATIO-TEMPORAL
DATA
MACHINE.
203
6.1.3 MAPPING INPUT TEMPORAL VARIABLES INTO A 3D
SNNCUBE BASED ON GRAPH MATCHING OPTIMISATION
A LGORITHM
.
211
6.2 DEEP LEARNING IN TIME-SPACE AND DEEP KNOWLEDGE
REPRESENTATION IN N EUC UBE
.
217
6.2.1 DEEP UNSUPERVISED LEARNING IN TIME-SPACE AND
DEEP KNOWLEDGE REPRESENTATION FROM TEMPORAL
OR SPATIO/SPECTRO TEMPORAL DATA (T S T D )
.
217
6.2.2 DEEP SUPERVISED LEARNING IN TIM E-SPACE
.
220
6.2.3 DEEP LEARNING IN TIME-SPACE FOR PREDICTIVE
MODELLING IN NEUCUBE. THE EPUSSS A LGORITHM
.
221
6.3 MODELLING
TIME
IN NEUCUBE: THE PAST, THE PRESENT,
THE FUTURE,. AND BACK TO THE P A S T
.
226
6.3.1 EVENT-BASED MODELLING. EXTERNAL VERSUS INTERNAL
TIME. PAST-, PRESENT- AND FUTURE TIM E
.
226
6.3.2 TRACING EVENTS BACK IN T IM E
. 227
6.4 A DESIGN METHODOLOGY FOR APPLICATION ORIENTED
SPATIO-TEMPORAL DATA M ACHINES
.
227
6.4.1 DESIGN METHODOLOGY FOR IMPLEMENTING APPLICATION
ORIENTED SPATIO-TEMPORAL DATA MACHINES
AS BI-AI SYSTEMS IN N E U C U B E
.
229
6.4.2 INPUT DATA
ENCODING. 230
6.4.3 SPATIAL MAPPING OF INPUT VARIABLES
.
232
6.4.4 UNSUPERVISED TRAINING OF THE SNNCUBE
.
233
6.4.5 SUPERVISED TRAINING AND CLASSIFICATION/REGRESSION OF
DYNAMIC SPIKING PATTERNS OF THE SNNCUBE IN A SNN
CLASSIFIER.
233
6.4.6 3D VISUALISATION OF THE S N N CUBE
.
234
6.4.7 OPTIMISATION OF NEUCUBE STRUCTURE
AND PARAMETERS
.
235
6.4.8 MODEL INTERPRETATION, RULE EXTRACTION,
DEEP IN TIME-SPACE KNOWLEDGE REPRESENTATION
.
236
6.5 CASE STUDIES OF THE DESIGN AND IMPLEMENTATION OF
CLASSIFICATION AND REGRESSION SPATIO-TEMPORAL
DATA M
ACHINES.
236
6.5.1 A CASE STUDY ON THE DESIGN OF A CLASSIFICATION
SPATIO-TEMPORAL DATA MACHINE IN N EUC UBE
.
237
6.5.2 A CASE STUDY ON THE DESIGN A REGRESSION/PREDICTION
SPATIO-TEMPORAL DATA MACHINE IN N EUC UBE
.
237
6.6 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
238
REFERENCES.
242
7 EVOLUTIONARY- AND QUANTUM-INSPIRED COMPUTATION. APPLICATIONS
FOR SNN
OPTIMISATION.
245
7.1 PRINCIPLES OF EVOLUTION AND METHODS OF EVOLUTIONARY
COMPUTATION
.
246
7.1.1 THE ORIGIN AND THE EVOLUTION OF L I F E
.
246
7.1.2 METHODS OF EVOLUTIONARY COMPUTATION ( E C ) . 247
7.1.3 GENETIC ALGORITHMS
. 249
7.1.4 EVOLUTIONARY STRATEGIES (E S
). 251
7.1.5 PARTICLE SWARM
OPTIMISATION. 252
7.1.6 ESTIMATION OF DISTRIBUTION ALGORITHMS (EDA). 254
7.1.7 ARTIFICIAL LIFE S YSTEM S
.
255
7.2 QUANTUM INSPIRED EVOLUTIONARY COMPUTATION: METHODS AND
ALGORITHMS.
256
7.2.1 PRINCIPLES OF QUANTUM INFORMATION PROCESSING
.
256
7.2.2 PRINCIPLES OF QUANTUM INSPIRED EVOLUTIONARY
ALGORITHMS (Q E A )
.
259
7.2.3 QUANTUM INSPIRED EVOLUTIONARY ALGORITHM (QIEA). . . 259
7.2.4 VERSATILE QIEA (V Q IE A
). 262
7.2.5 EXTENSION OF THE VQIEA TO DEAL WITH CONTINUOUS
VALUE V
ARIABLES.
265
7.3 QUANTUM INSPIRED EVOLUTIONARY COMPUTATION FOR THE
OPTIMISATION OF S N N
.
268
7.3.1 A QUANTUM-INSPIRED REPRESENTATION OF A S N N
.
268
7.3.2 APPLICATION OF QIEA FOR THE OPTIMISATION
OF ESNN CLASSIFIER ON ECOLOGICAL D A TA
.
271
7.3.3 INTEGRATIVE COMPUTATIONAL NEURO GENETIC MODEL
(CNGM) UTILISING QUANTUM-INSPIRED
REPRESENTATION
.
272
7.4 QUANTUM INSPIRED PARTICLE SWARM OPTIM ISATION
.
274
7.4.1 QUANTUM INSPIRED PARTICLE SWARM OPTIMISATION
ALGORITHMS
.
274
7.4.2 QUANTUM INSPIRED PARTICLE SWARM OPTIMISATION
ALGORITHM (QIPSO) FOR THE OPTIMISATION OF ESNN. . . . 275
7.4.3 DYNAMIC
QIPSO.
277
7.4.4 APPLICATION OF DQIPSO FOR FEATURE SELECTION AND
MODEL O PTIM
ISATION. 278
7.5 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
282
REFERENCES.
285
PART IV DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION OF
BRAIN DATA
8 DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION
OF EEG D A TA
.
291
8.1 TIME-SPACE BRAIN DATA. EEG D A T A
.
291
8.1.1 SPATIO-TEMPORAL BRAIN D ATA
.
291
8.1.2 BRAIN A
TLASES.
292
8.1.3 EEG D A TA
.
295
8.2 DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION
OF EEG DATA IN B I-SN N
. 300
8.3 DEEP LEARNING, RECOGNITION AND MODELLING OF COGNITIVE
T
ASKS.
306
8.3.1 SYSTEM D ESIGN
.
306
8.3.2 CASE STUDY COGNITIVE EEG D
ATA. 309
8.3.3 EXPERIMENTAL R E SU
LTS. 309
8.3.4 MODEL
INTERPRETATION.
311
8.4 DEEP LEARNING, RECOGNITION AND EXPRESSION OF EMOTIONS
IN A
BI-SNN.
312
8.4.1 GENERAL N
OTIONS.
312
8.4.2 USING A NEUCUBE MODEL FOR EMOTION RECOGNITION. . . . 313
8.4.3 A CASE STUDY OF EEG DATA FOR EMOTION RECOGNITION
FROM FACIAL EXPRESSION
.
314
8.4.4 ANALYSIS OF THE CONNECTIVITY IN A TRAINED SNNCUBE
WHEN A PERSON IS PERCEIVING EMOTIONAL FACE AND
WHEN A PERSON IS EXPRESSING SUCH EMOTIONS. 314
8.4.5 CAN WE TEACH A MACHINE TO EXPRESS EM OTIONS?
.
317
8.5 DEEP LEARNING AND MODELLING OF PERI-PERCEPTUAL PROCESSES
IN B I-S N N
.
317
8.5.1 THE PSYCHOLOGY OF SUB-CONSCIOUS BRAIN PROCESSES . 318
8.5.2 EXPERIMENTAL SETTING AND EEG DATA C OLLECTION
.
319
8.5.3 THE DESIGN OF A NEUCUBE M O D E L
.
321
8.6 MODELLING ATTENTIONAL BIAS IN BI-SNN
.
328
8.6.1 ATTENTIONAL B IA S
. 328
8.6.2 EXPERIMENTAL
SETTINGS. 328
8.6.3 RESULTS
.
328
8.7 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
330
REFERENCES.
333
9 B RAIN DISEASE DIAGNOSIS AND PROGNOSIS BASED ON EEG D A T A
.
339
9.1 SNN FOR MODELLING EEG DATA TO ASSESS A POTENTIAL
PROGRESSION FROM MCI TO A D
. 339
9.1.1 DESIGN OF THE STUDY AND DATA COLLECTION.
340
9.1.2 DESIGN OF A NEUCUBE
MODEL. 340
9.1.3 CLASSIFICATION R ESU
LTS. 343
9.1.4 ANALYSIS OF FUNCTIONAL CHANGES IN BRAIN ACTIVITY
FROM MCI TO A D
. 344
9.2 SNN FOR PREDICTIVE MODELLING OF RESPONSE TO TREATMENT
USING EEG D
ATA.
344
9.2.1 CONCEPTUAL D ESIGN
.
345
9.2.2 THE CASE STUDY PROBLEM SPECIFICATION AND DATA
C OLLECTION
.
345
9.2.3 MODELLING THE EEG DATA IN A NEUCUBE M O D E L
.
348
9.2.4 COMPARATIVE ANALYSIS OF BRAIN ACTIVITIES OF MMT
SUBJECTS UNDER DIFFERENT DRUG DOSES VERSUS CO
AND OP SUBJECTS. MODELLING AND UNDERSTANDING THE
INFORMATION EXCHANGE BETWEEN BRAIN AREAS
MEASURED THROUGH EEG CHANNELS
.
352
9.2.5 ANALYSIS OF CLASSIFICATION R ESU
LTS. 355
9.3 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
356
REFERENCES.
357
10 DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION
O F F M R I D A T A
.
361
10.1 BRAIN FMRI DATA AND THEIR ANALYSIS
.
361
10.1.1 WHAT ARE FMRI D A TA ?
.
361
10.1.2 TRADITIONAL METHODS FOR FMRI DATA A NALYSIS. 363
10.1.3 SELECTING FEATURES FROM FMRI D A T A
. 365
10.2 DEEP LEARNING AND DEEP KNOWLEDGE REPRESENTATION
OF FMRI DATA IN
NEUCUBE.
366
10.2.1 WHY USE SNN FOR MODELLING OF FMRI
SPATIO-TEMPORAL BRAIN D A TA
?. 366
10.2.2 A METHODOLOGY FOR DEEP LEARNING AND DEEP
KNOWLEDGE REPRESENTATION OF FMRI DATA
IN
BI-SNN.
367
10.3 MAPPING, LEARNING AND CLASSIFICATION OF FMRI DATA IN
NEUCUBE ON THE CASE STUDY OF STAR/PLUS D A TA .
370
10.3.1 THE STAR/PLUS BENCHMARK FMRI DATA. 370
10.3.2 FMRI DATA ENCODING, MAPPING AND LEARNING
IN A NEUCUBE SNN M O D E L
.
371
10.3.3 CLASSIFICATION OF THE FMRI DATA IN A NEUCUBE-BASED
M O D E
L.
377
10.4 ALGORITHMS FOR MODELLING FMRI DATA THAT MEASURE COGNITIVE
PROCESSES.
379
10.4.1 ALGORITHM FOR ENCODING DYNAMIC STBD INTO SPIKE
S
EQUENCES.
380
10.4.2 CONNECTIVITY INITIALIZATION AND DEEP LEARNING IN A
SNN C
UBE.
380
10.4.3 DEEP KNOWLEDGE REPRESENTATION IN A TRAINED SNN
M O D E
L.
383
10.4.4 A CASE STUDY IMPLEMENTATION ON THE STAR/PLUS
D A T A
.
383
10.5 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
388
REFERENCES.
391
11 INTEGRATING TIME-SPACE AND ORIENTATION. A CASE STUDY ON
FM RI + DTI B RAIN D A T A
.
397
11.1 INTRODUCTION AND BACKGROUND W O RK
.
397
11.2 A PERSONALISED MODELLING ARCHITECTURE FOR FMRI AND DTI DATA
INTEGRATION BASED ON THE NEUCUBE B I-S N N
.
400
11.3 ORIENTATION-INFLUENCE DRIVEN STDP (OISTDP) LEARNING IN
SNN FOR THE INTEGRATION OF TIME-SPACE AND DIRECTION,
ILLUSTRATED ON FMRI AND DTI D A TA
.
402
11.3.1 ARCHITECTURE, MAPPING AND INITIALIZATION S CHEM E
.
403
11.3.2 NEURON M O D E L
.
403
11.3.3 UNSUPERVISED WEIGHT ADAPTATION OF S YNAPSES
.
406
11.4 EXPERIMENTAL RESULTS ON SYNTHETIC D A TA
.
412
11.4.1 DATA D
ESCRIPTION.
412
11.4.2 EXPERIMENTAL R E SU
LTS. 412
11.5 USING OISTDP LEARNING FOR THE CLASSIFICATION OF RESPONDING
AND NON-RESPONDING SCHIZOPHRENIC PATIENTS TO CLOZAPINE
M
ONOTHERAPY.
414
11.5.1 PROBLEM SPECIFICATION AND DATA PREPARATION
.
414
11.5.2 MODELLING AND EXPERIMENTAL R
ESULTS. 417
11.6 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
420
R
EFERENCES.
421
PART V SNN FOR AUDIO-VISUAL DATA AND BRAIN-COMPUTER INTERFACES
12 AUDIO- AND VISUAL INFORMATION PROCESSING IN THE BRAIN AND ITS
MODELLING WITH EVOLVING SN N
. 431
12.1 AUDIO AND VISUAL INFORMATION PROCESSING
IN THE HUMAN B R A I N
.
431
12.1.1 AUDIO INFORMATION PROCESSING
.
432
12.1.2 VISUAL INFORMATION
PROCESSING. 434
12.1.3 INTEGRATED AUDIO AND VISUAL INFORMATION
PROCESSING.
437
12.2 MODELLING AUDIO-, VISUAL AND AUDIO-VISUAL INFORMATION
PROCESSING WITH CONVOLUTIONAL EVOLVING SPIKING NEURAL
NETWORKS
(CESNN).
440
12.2.1 ISSUES WITH MODELLING AUDIO-VISUAL INFORMATION WITH
S N N
.
440
12.2.2 CONVOLUTIONAL ESNN (CESNN) FOR MODELLING VISUAL
INFORM
ATION.
442
12.2.3 CONVOLUTIONAL ESNN (CESNN) FOR MODELLING
AUDIO INFORM ATION
.
443
12.2.4 CONVOLUTIONAL ESNN (CESNN) FOR INTEGRATED
AUDIO-VISUAL INFORMATION PROCESSING. 444
12.3 CASE STUDIES, EXPERIMENTS AND RESULTS
.
448
12.3.1 DATA S
ETS.
448
12.3.2 EXPERIMENTAL R E SU
LTS. 449
12.4 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
453
REFERENCES.
455
13 DEEP LEARNING AND MODELLING OF AUDIO-, VISUAL-, AND MULTIMODAL
AUDIO-VISUAL DATA IN BRAIN-INSPIRED S N N
. 457
13.1 DEEP LEARNING OF SOUND IN BRAIN-INSPIRED SN N
.
457
13.1.1 DEEP LEARNING OF AUDIO DATA IN THE B R A I N
.
457
13.1.2 A BI-SNN USING TONOTOPIC AND STEREO MAPPING AND
LEARNING OF S O U N D
.
459
13.1.3 DEEP LEARNING AND RECOGNITION OF M U S IC
.
459
13.1.4 EXPERIMENTAL R E SU
LTS. 460
13.2 DEEP LEARNING AND RECOGNITION OF VISUAL DATA IN A
BRAIN-INSPIRED SNN FOR FAST MOVING OBJECT RECOGNITION
AND FOR GENDER RECOGNITION
.
462
13.2.1 TWO APPROACHES TO VISUAL INFORMATION PROCESSING. . . . 462
13.2.2 APPLICATIONS FOR FAST MOVING OBJECT RECOGNITION . 463
13.2.3 APPLICATIONS FOR GENDER AND AGE GROUP
CLASSIFICATION BASED ON FACE R ECOGNITION
.
464
13.3 RETINOTOPIC MAPPING AND LEARNING OF DYNAMIC VISUAL
INFORMATION IN A BRAIN-LIKE SNN ARCHITECTURE ON THE CASE
STUDY OF MOVING OBJECT
RECOGNITION. 467
13.3.1 GENERAL P RINCIPLES
.
467
13.3.2 THE BRAIN-INSPIRED SNN AND THE PROPOSED
RETINOTOPIC M AP P IN G
. 467
13.3.3 UNSUPERVISED AND SUPERVISED LEARNING
OF DYNAMIC VISUAL PATTERNS
.
469
13.3.4 DESIGN OF AN EXPERIMENT FOR THE MNIST-DVS
BENCHMARK DATASET
.
470
13.3.5 EXPERIMENTAL R E SU
LTS. 471
13.3.6 MODEL INTERPRETATION FOR A BETTER UNDERSTANDING OF THE
PROCESSES INSIDE THE VISUAL C O RTE X
.
472
13.3.7 SUMMARY OF THE PROPOSED BI-SNN RETINOTOPIC
MAPPING M ETH O D
. 473
13.4 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE
.
473
REFERENCES.
474
14 BRAIN-COMPUTER INTERFACES USING BRAIN-INSPIRED S N N
.
479
14.1 BRAIN-COMPUTER
INTERFACES.
479
14.1.1 GENERAL N
OTIONS.
479
14.1.2 BCI BASED ON E E G
. 481
14.1.3 TYPES AND APPLICATIONS OF B C I
.
481
14.2 A FRAMEWORK FOR BRAIN-INSPIRED BCI (B I-B C I)
.
485
14.2.1 THE NEUCUBE BI-SNN ARCHITECTURE
.
485
14.2.2 A BRAIN-INSPIRED FRAMEWORK FOR BCI (BI-BCI) WITH
NEUROFEEDBACK
.
488
14.3 BI-BCI FOR DETECTING MOTOR EXECUTION AND MOTOR INTENTION
FROM EEG
SIGNALS.
489
14.3.1
INTRODUCTION.
489
14.3.2 DESIGN OF AN EXPERIMENTAL BI-BCI S Y STE M
.
491
14.3.3 CLASSIFICATION R ESU
LTS. 492
14.3.4 ANALYSIS OF THE R ESULTS
.
492
14.4 BI-BCI FOR NEUROREHABILITATION WITH A NEUROFEEDBACK
AND FOR NEURO-PROSTHETICS
.
494
14.4.1 GENERAL N
OTIONS.
494
14.4.2
APPLICATIONS.
496
14.5 FROM BI-BCI TO KNOWLEDGE TRANSFER BETWEEN HUMANS
AND M
ACHINES.
498
14.6 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
499
REFERENCES.
499
PART VI SNN IN BIO- AND NEUROINFORMATICS
15 COMPUTATIONAL MODELLING AND PATTERN RECOGNITION IN
BIOINFORMATICS.
505
15.1 BIOINFORMATICS P RIM
ER.
505
15.1.1 GENERAL N
OTIONS.
505
15.1.2 DNA, RNA AND PROTEINS. THE CENTRAL DOGMA OF
MOLECULAR BIOLOGY AND THE EVOLUTION OF LIFE
.
506
15.1.3
PHYLOGENETICS.
512
15.1.4 THE CHALLENGES OF MOLECULAR DATA ANALYSIS
.
513
15.2 BIOLOGICAL DATABASES. COMPUTATIONAL MODELLING OF
BIOINFORMATICS D A T A
.
516
15.2.1 BIOLOGICAL DATABASES
. 516
15.2.2 GENERAL INFORMATION ABOUT BIOINFORMATICS DATA
M ODELLING
.
517
15.2.3 GENE EXPRESSION DATA MODELLING AND PROFILING
.
519
15.2.4 CLUSTERING OF TIME SERIES GENE EXPRESSION D A TA
.
521
15.2.5 PROTEIN DATA MODELLING AND STRUCTURE PREDICTION
.
523
15.3 GENE AND PROTEIN INTERACTION NETWORKS AND THE SYSTEM
BIOLOGY
APPROACH.
524
15.3.1 GENERAL N
OTIONS.
524
15.3.2 GENE REGULATORY NETWORK MODELLING
.
526
15.3.3 PROTEIN INTERACTION
NETWORKS. 527
15.4 BRAIN-INSPIRED SNN ARCHITECTURES FOR DEEP LEARNING OF GENE
EXPRESSION TIME SERIES DATA AND FOR THE EXTRACTION OF GENE
REGULATORY NETWORKS
.
529
15.4.1 GENERAL N
OTIONS.
529
15.4.2 A SNN BASED METHODOLOGY FOR GENE EXPRESSION
TIME SERIES DATA MODELLING AND EXTRACTING GRN . 530
15.4.3 EXTRACTING GRN FROM A TRAINED M O D EL
.
532
15.4.4 A CASE STUDY EXPERIMENTAL MODELLING OF GENE
EXPRESSION TIME SERIES DATA.
533
15.4.5 EXTRACTING GRN FORM A TRAINED MODEL AND ANALYSIS
OF THE GRN FOR NEW KNOWLEDGE DISCOVERY
.
535
15.4.6 DISCUSSIONS ON THE M ETHOD
.
538
15.5 CHAPTER SUMMARY AND FURTHER READINGS
. 539
REFERENCES.
540
16 COMPUTATIONAL NEURO-GENETIC M ODELLING
.
545
16.1 COMPUTATIONAL N
EUROGENETICS.
545
16.1.1 GENERAL N
OTIONS.
545
16.2 PROBABILISTIC NEUROGENETIC MODEL (PNGM) OF A SPIKING
NEURON.
548
16.2.1 THE PNGM OF A SPIKING
NEURON. 548
16.2.2 USING THE PNGM OF A NEURON TO BUILD SN N . 551
16.3 COMPUTATIONAL NEUROGENETIC MODELLING (CNGM)
A
RCHITECTURES.
552
16.3.1 CNGM A
RCHITECTURES.
552
16.3.2 THE NEUCUBE ARCHITECTURE AS A CNGM
.
553
16.4 APPLICATIONS OF C N G M
.
555
16.4.1 MODELLING BRAIN D
ISEASES. 555
16.4.2 CNGM FOR COGNITIVE ROBOTICS AND EMOTIONAL
COMPUTING.
556
16.5 LIFE, DEATH AND
CNGM.
557
16.6 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
558
REFERENCES.
559
17 A COMPUTATIONAL FRAMEWORK FOR PERSONALISED MODELLING.
APPLICATIONS IN BIOINFORM
ATICS.
563
17.1 A FRAMEWORK FOR PM AND PERSON PROFILING BASED ON
INTEGRATED FEATURE AND MODEL PARAMETER OPTIMISATION
.
563
17.1.1 INTRODUCTION: GLOBAL, LOCAL AND PERSONALISE
M
ODELLING.
563
17.1.2 A FRAMEWORK FOR PERSONALISED MODELLING (PM) BASED
ON INTEGRATED FEATURE AND MODEL PARAMETER
O PTIM
ISATION.
565
17.2 PM FOR GENE EXPRESSION DATA CLASSIFICATION USING TRADITIONAL
A N N
.
573
17.2.1 PROBLEM AND DATA SPECIFICATION, FEATURE EXTRACTION . . . 573
17.2.2 CLASSIFICATION ACCURACY AND COMPARATIVE
ANALYSIS. . . 573
17.2.3 PROFILING OF INDIVIDUALS AND PERSONALISED KNOWLEDGE
E
XTRACTION.
576
17.3 PM ON BIOMEDICAL DATA USING EVOLVING S N N
.
576
17.3.1 INTRODUCTION
.
576
17.3.2 USING SNN AND ESNN FOR P M
.
578
17.3.3 AN ESNN METHOD FOR PM ON BIOMEDICAL D A TA
.
580
17.3.4 A CASE STUDY OF PM FOR CHRONIC KIDNEY DISEASE
DATA CLASSIFICATION
.
585
17.4 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
587
R EFERENCES. 588
18 PERSONALISED MODELLING FOR INTEGRATED STATIC AND DYNAMIC DATA.
APPLICATIONS IN
NEUROINFORMATICS.
593
18.1 A FRAMEWORK FOR PM BASED ON BI-SNN ARCHITECTURE FOR
INTEGRATED STATIC AND DYNAMIC DATA M ODELLING
.
593
18.1.1
INTRODUCTION.
593
18.1.2 A NEUCUBE-BASED FRAMEWORK FOR PM OF INTEGRATED
STATIC AND DYNAMIC D A T A
. 595
18.1.3 COMPARATIVE ANALYSIS OF THE NEUCUBE BASED METHOD
WITH OTHER METHODS FOR P M .
598
18.2 PERSONALISED DEEP LEARNING AND KNOWLEDGE REPRESENTATION IN
TIME-SPACE. A CASE ON INDIVIDUAL STROKE RISK PREDICTION
.
599
18.2.1 THE CASE STUDY DATA FOR INDIVIDUAL STROKE RISK
P RED ICTIO N
.
599
18.2.2 PERSONALISED DEEP LEARNING AND KNOWLEDGE
REPRESENTATION IN NEUCUBE ON THE CASE OF STROKE . 601
18.3 PM FOR PREDICTING RESPONSE TO TREATMENT USING PERSONAL DATA
AND EEC SPATIO-TEMPORAL D ATA
.
604
18.3.1 THE CASE STUDY PROBLEM AND D A TA
.
604
18.3.2 THE NEUCUBE BASED PM M O D E
L. 605
18.3.3 EXPERIMENTAL R E SU
LTS. 606
18.3.4 D
ISCUSSIONS.
607
18.4 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
609
R
EFERENCES.
610
PART VII DEEP IN TIME-SPACE LEARNING AND DEEP KNOWLEDGE
REPRESENTATION OF MULTISENSORY STREAMING DATA
19 DEEP LEARNING OF MULTISENSORY STREAMING DATA FOR PREDICTIVE
MODELLING WITH APPLICATIONS IN FINANCE, ECOLOGY, TRANSPORT AND
ENVIRONMENT.
619
19.1 A GENERAL FRAMEWORK FOR DEEP LEARNING AND PREDICTIVE
MODELLING OF MULTISENSORY TIME-SPACE STREAMING DATA WITH
S N N
.
619
19.1.1 THE CHALLENGES OF PATTERN RECOGNITION AND MODELLING
OF MULTISENSORY STREAMING D A T A
.
620
19.1.2 MODELLING STREAMING DATA IN EVOLVING
SNN (ESN N
).
621
19.1.3 A GENERAL METHODOLOGY FOR MODELLING MULTISENSORY
STREAMING DATA IN BRAIN-INSPIRED SNN FOR
CLASSIFICATION AND R EGRESSION
.
622
19.2 STOCK MARKET MOVEMENT PREDICTION USING ON-LINE PREDICTIVE
MODELLING WITH ESN N
.
628
19.3 SNN FOR DEEP LEARNING AND PREDICTIVE MODELLING OF
ECOLOGICAL STREAMING D A T A
.
631
19.3.1 EARLY EVENT PREDICTION IN ECOLOGY:
GENERAL N
OTIONS.
631
19.3.2 A CASE STUDY ON PREDICTING ABUNDANCE OF FRUIT FLIES
USING SPATIO-TEMPORAL CLIMATE D A TA . 632
19.4 SNN FOR DEEP LEARNING AND PREDICTIVE MODELLING OF TRANSPORT
STREAMING D ATA
.
638
19.4.1 A CASE STUDY TRANSPORT MODELLING P RO B LEM . 638
19.4.2 NEUCUBE MODEL CREATION AND MODELLING R ESULTS
.
638
19.5 SNN FOR PREDICTIVE MODELLING OF SEISMIC DATA
.
642
19.5.1 THE CHALLENGE OF PREDICTING HAZARDOUS E V E N TS
.
642
19.5.2 PREDICTIVE MODELLING OF SEISMIC DATA FOR EARTHQUAKE
FORECASTING USING N EUC UBE.
642
19.5.3 EXPERIMENT D ESIGN
.
644
19.5.4 D
ISCUSSIONS.
648
19.6 FUTURE APPLICATIONS
.
649
19.6.1 MODELLING MULTISENSORY AIR POLLUTION
STREAMING D A T A
. 649
19.6.2 WIND ENERGY PREDICTION FROM WIND T U RB IN E S
.
651
19.6.3 SNN FOR RADIO-ASTRONOMY DATA M ODELLING
.
651
19.7 CHAPTER SUMMARY AND FURTHER READINGS FOR DEEPER
KNOWLEDGE.
651
REFERENCES.
655
P ART V III FUTURE DEVELOPMENT IN BI-SNN AND BI-AI
20 FROM VON NEUMANN MACHINES TO NEUROMORPHIC P LA TFO RM S
.
661
20.1 PRINCIPLES OF COMPUTATION. THE VON NEUMANN MACHINES AND
B E Y O N D
.
661
20.1.1 GENERAL N
OTIONS.
661
20.1.2 THE VON NEUMANN COMPUTATION PRINCIPLE AND THE
ATANASSOV*S ABC M ACHINE
.
662
20.1.3 GOING BEYOND VON NEUMANN PRINCIPLES AND ABC
COMPUTER.
664
20.2 NEUROMORPHIC COMPUTATION AND PLATFORM S
.
664
20.2.1 GENERAL P RINCIPLES
.
664
20.2.2 HARDWARE PLATFORMS FOR NEUROMORPHIC
C OM
PUTATION.
665
20.3 SNN DEVELOPMENT SYSTEMS. NEUCUBE AS A DEVELOPMENT
SYSTEM FOR SPATIO-TEMPORAL DATA MACHINES
.
667
20.3.1 A BRIEF OVERVIEW OF SNN DEVELOPMENT SYSTEMS . 667
20.3.2 THE NEUCUBE DEVELOPMENT SYSTEM FOR
SPATIO-TEMPORAL DATA M ACHINES
.
669
20.3.3 IMPLEMENTATION OF NEUCUBE-BASED SPATIO-TEMPORAL
DATA MACHINES ON TRADITIONAL AND ON NEUROMORPHIC
HARDWARE P LATFORM
S. 672
20.4 CHAPTER SUMMARY AND FURTHER READINGS
. 673
REFERENCES.
674
21 FROM CLAUDE SHANNON*S INFORM ATION ENTROPY TO SPIKE-TIME
DATA COMPRESSION T H E O R Y
.
679
21.1 CLAUDE SHANNON*S CLASSICAL INFORMATION THEORY
.
679
21.2 THE PROPOSED INFORMATION THEORY FOR TEMPORAL DATA
COMPRESSION FOR CLASSIFICATION TASKS BASED ON SPIKE-TIME
E
NCODING.
681
21.3 A SPIKE-TIME ENCODING AND COMPRESSION METHOD FOR FMRI
SPATIO-TEMPORAL DATA CLASSIFICATION
.
685
21.4 CHAPTER SUMMARY AND FURTHER READINGS
. 695
REFERENCES.
697
22 FROM BRAIN-INSPIRED AI TO A SYMBIOSIS OF HUM AN INTELLIGENCE
AND ARTIFICIAL IN TELLIG EN
CE.
701
22.1 TOWARDS INTEGRATED QUANTUM-MOLECULAR-NEUROGENETIC-BRAIN-
INSPIRED M O D
ELS.
701
22.1.1 QUANTUM COM PUTATION
.
702
22.1.2 THE CONCEPT OF AN INTEGRATED QUANTUM-NEUROGENETIC-
BRAIN-INSPIRED MODEL BASED ON S N N
.
704
22.2 TOWARDS A SYMBIOSIS BETWEEN HUMAN INTELLIGENCE AND
ARTIFICIAL INTELLIGENCE (HI + AI), LED BY H I
.
707
22.2.1 SOME NOTIONS ABOUT
AGI. 707
22.2.2 TOWARDS A SYMBIOSIS BETWEEN HUMAN INTELLIGENCE
AND ARTIFICIAL INTELLIGENCE (HI + AI), LED BY H I
.
707
22.3 SUMMARY AND FURTHER READINGS FOR A DEEPER KNOW LEDGE
.
711
REFERENCES.
711
EPILOGUE.
715
G
LOSSARY.
717
IN D E X
.
735 |
| any_adam_object | 1 |
| author | Kasabov, Nikola K. 1948- |
| author_GND | (DE-588)120156016 |
| author_facet | Kasabov, Nikola K. 1948- |
| author_role | aut |
| author_sort | Kasabov, Nikola K. 1948- |
| author_variant | n k k nk nkk |
| building | Verbundindex |
| bvnumber | BV046445283 |
| ctrlnum | (OCoLC)1062435893 (DE-599)DNB1159913188 |
| dewey-full | 006.32 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.32 |
| dewey-search | 006.32 |
| dewey-sort | 16.32 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Biologie Informatik Medizin |
| format | Book |
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| id | DE-604.BV046445283 |
| illustrated | Illustrated |
| indexdate | 2025-02-13T07:00:28Z |
| institution | BVB |
| institution_GND | (DE-588)1065168780 |
| isbn | 9783662577134 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031857272 |
| oclc_num | 1062435893 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM |
| owner_facet | DE-19 DE-BY-UBM |
| physical | xxxiv, 738 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Springer |
| record_format | marc |
| series | Springer series on bio- and neurosystems |
| series2 | Springer series on bio- and neurosystems |
| spelling | Kasabov, Nikola K. 1948- Verfasser (DE-588)120156016 aut Time-space, spiking neural networks and brain-inspired artificial intelligence Nikola K. Kasabov Berlin Springer [2019] xxxiv, 738 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Springer series on bio- and neurosystems Volume 7 Neuroinformatik (DE-588)4655105-0 gnd rswk-swf Deep Learning (DE-588)1135597375 gnd rswk-swf Datenstrom (DE-588)4410055-3 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Gehirn-Computer-Schnittstelle (DE-588)4616897-7 gnd rswk-swf Hirnfunktion (DE-588)4159930-5 gnd rswk-swf Pulsverarbeitendes neuronales Netz (DE-588)4529621-2 gnd rswk-swf Wissensrepräsentation (DE-588)4049534-6 gnd rswk-swf Elektroencephalogramm (DE-588)4070747-7 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf COM004000 UYQ COM014000 MED057000 TEC037000 COM016000 PSA PSAN TJFM1 UYQP B SCT11014: Computational Intelligence SUCO11647: Engineering SCI23050: Computational Biology/Bioinformatics SCB18006: Neurosciences SCT19020: Robotics and Automation SCI2203X: Pattern Recognition Technik/Allgemeines, Lexika Pulsverarbeitendes neuronales Netz (DE-588)4529621-2 s Deep Learning (DE-588)1135597375 s Wissensrepräsentation (DE-588)4049534-6 s Hirnfunktion (DE-588)4159930-5 s Elektroencephalogramm (DE-588)4070747-7 s Gehirn-Computer-Schnittstelle (DE-588)4616897-7 s Mustererkennung (DE-588)4040936-3 s Datenstrom (DE-588)4410055-3 s Bioinformatik (DE-588)4611085-9 s Neuroinformatik (DE-588)4655105-0 s DE-604 Springer-Verlag GmbH (DE-588)1065168780 pbl Erscheint auch als Online-Ausgabe 978-3-662-57715-8 Springer series on bio- and neurosystems Volume 7 (DE-604)BV045520784 7 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031857272&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kasabov, Nikola K. 1948- Time-space, spiking neural networks and brain-inspired artificial intelligence Springer series on bio- and neurosystems Neuroinformatik (DE-588)4655105-0 gnd Deep Learning (DE-588)1135597375 gnd Datenstrom (DE-588)4410055-3 gnd Mustererkennung (DE-588)4040936-3 gnd Gehirn-Computer-Schnittstelle (DE-588)4616897-7 gnd Hirnfunktion (DE-588)4159930-5 gnd Pulsverarbeitendes neuronales Netz (DE-588)4529621-2 gnd Wissensrepräsentation (DE-588)4049534-6 gnd Elektroencephalogramm (DE-588)4070747-7 gnd Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)4655105-0 (DE-588)1135597375 (DE-588)4410055-3 (DE-588)4040936-3 (DE-588)4616897-7 (DE-588)4159930-5 (DE-588)4529621-2 (DE-588)4049534-6 (DE-588)4070747-7 (DE-588)4611085-9 |
| title | Time-space, spiking neural networks and brain-inspired artificial intelligence |
| title_auth | Time-space, spiking neural networks and brain-inspired artificial intelligence |
| title_exact_search | Time-space, spiking neural networks and brain-inspired artificial intelligence |
| title_full | Time-space, spiking neural networks and brain-inspired artificial intelligence Nikola K. Kasabov |
| title_fullStr | Time-space, spiking neural networks and brain-inspired artificial intelligence Nikola K. Kasabov |
| title_full_unstemmed | Time-space, spiking neural networks and brain-inspired artificial intelligence Nikola K. Kasabov |
| title_short | Time-space, spiking neural networks and brain-inspired artificial intelligence |
| title_sort | time space spiking neural networks and brain inspired artificial intelligence |
| topic | Neuroinformatik (DE-588)4655105-0 gnd Deep Learning (DE-588)1135597375 gnd Datenstrom (DE-588)4410055-3 gnd Mustererkennung (DE-588)4040936-3 gnd Gehirn-Computer-Schnittstelle (DE-588)4616897-7 gnd Hirnfunktion (DE-588)4159930-5 gnd Pulsverarbeitendes neuronales Netz (DE-588)4529621-2 gnd Wissensrepräsentation (DE-588)4049534-6 gnd Elektroencephalogramm (DE-588)4070747-7 gnd Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Neuroinformatik Deep Learning Datenstrom Mustererkennung Gehirn-Computer-Schnittstelle Hirnfunktion Pulsverarbeitendes neuronales Netz Wissensrepräsentation Elektroencephalogramm Bioinformatik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031857272&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV045520784 |
| work_keys_str_mv | AT kasabovnikolak timespacespikingneuralnetworksandbraininspiredartificialintelligence AT springerverlaggmbh timespacespikingneuralnetworksandbraininspiredartificialintelligence |