Page 323 - Artificial Intelligence in the Age of Neural Networks and Brain Computing

316 Index

D future directions of research, 236e239
DAD. See Dementia Alzheimer Disease (DAD) interpretability, 238
DarkCycle, 308e309 models for EEG signal processing, 232e236
Darknet need and opportunity for DL revolution in
analysis to capturing malicious cyberattack neuroscience, 163e164
behaviors, 258e261 neural network approach, 220e222
traffic pattern, 260, 260f and neural networks before (2009e11), 162
DARPA, 57, 59e60 other DL approaches, 236
Darwin’s theory of evolution, 2e3 robustness of DL networks, 239
Darwinian mechanism, 56 techniques, 146, 303
Deep learning neural networks (DNN), 127,
Data availability, correct, 246e247
215e216
Data crap, 142e146
Deep multimodal feature learning, 275e277
Data type, 268
deep learning application to predict patient’s
DAV. See Driverless autonomous vehicles (DAV)
survival, 276
DBNs. See Deep Belief Networks (DBNs)
multimodal neuroimaging feature learning,
DDoS. See Distributed Denial of Service
276e277
Attack (DDoS)
Deep neural networks (DNNs), 275e276,
Decision directed learning, 7
293e296. See also Artificial neural
for channel equalization, 9f
network (ANN); Convolutional neural
Deductive reasoning, 112
networks (CNN)
Deep architectures and learning, 222e226
application case study, 302e305
CNN, 224e226
stacked autoencoders, 224 deep learning architectures evolution, 296e300
Deep Belief Networks (DBNs), 223 LSTM architectures evolution, 300e301
Deep Blue supercomputer (IBM), 205e206 DeepNEAT, 296e297
Deep Convolution Neural Networks (CNN), 223, Dementia Alzheimer Disease (DAD), 66e67,
236 73e74
Deep learning (DL), 60, 66e67, 69, 165e166, Dendritic trees, 96
170e172, 206e208, 220. See also DENFIS. See Dynamic evolving neurofuzzy
Multiview learning inference system (DENFIS)
advanced learning approaches in, 238e239 Density-based spatial clustering, 21
algorithms, 54 deSNN. See Dynamic eSNN (deSNN)
architectures evolution, 296e300 Desynchronization event, 215
cooperative coevolution of modules and “Detect & react” approaches, 252
blueprints, 297e298 Device approach, 85e86
DNNs in CIFAR-10 benchmark, 298e300, Dichotomies, 79e84
brain-scomputer analogy/disanalogy, 81e83
299t, 300f
brain-mind problem, 80e81
extending NEAT to deep networks, 296e297
computational theory of mind, 83e84
cultural revolution and opportunities,
Differentiability, 251
162e163
Digital communication, 10
deep architectures and learning, 222e226
Digital computers, 206
and deep knowledge representation in NeuCube
Digital revolution, 246
SNN models, 127e129
Dipole wave, 96
semisupervised learning, 129
Disambiguation module, 256
supervised learning for classification of learned
Distributed Denial of Service Attack (DDoS),
patterns, 128
257e258, 259f
electrophysiological time-series, 226e232
DL. See Deep learning (DL)
foundations of DL revolution
DNN. See Deep learning neural networks (DNN)
backpropagation, 168e170, 170f
DNNs. See Deep neural networks (DNNs)
CoNNs, and autoencoders, 170e172
current landscape, 164e166 Downward causation, 81, 82f
deep revolution, 167e168 Dreyfus’ situated intelligence approach, 208