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Author Rolls, Edmund T., author
Title Cerebral cortex : principles of operation / Edmund T. Rolls
Imprint Oxford ; New York, NY : Oxford University Press, 2016
book jacket
Edition First edition
Descript 1 online resource (xix, 958 pages ): illustrations
text txt rdacontent
computer rdamedia
online resource rdacarrier
Note Includes bibliographical references (pages 890-949) and index
1.1. Principles of operation of the cerebral cortex: introduction and plan -- 1.2. Neurons -- 1.3. Neurons in a network -- 1.4. Synaptic modification -- 1.5. Long-term potentiation and long-term depression -- 1.6. Distributed representations -- 1.6.1. Definitions -- 1.6.2. Advantages of different types of coding -- 1.7. Neuronal network approaches versus connectionism -- 1.8. Introduction to three neuronal network architectures -- 1.9. Systems-level analysis of brain function -- 1.9.1. Ventral cortical visual stream -- 1.9.2. Dorsal cortical visual stream -- 1.9.3. Hippocampal memory system -- 1.9.4. Frontal lobe systems -- 1.9.5. Brodmann areas -- 1.10. fine structure of the cerebral neocortex -- 1.10.1. fine structure and connectivity of the neocortex -- 1.10.2. Excitatory cells and connections -- 1.10.3. Inhibitory cells and connections -- 1.10.4. Quantitative aspects of cortical architecture -- 1.10.5. Functional pathways through the cortical layers -- 1.10.6. scale of lateral excitatory and inhibitory effects, and modules -- 1.11. Highlights -- 2.1. Introduction -- 2.2. Hierarchical organization in sensory systems -- 2.2.1. Hierarchical organization in the ventral visual system -- 2.2.2. Hierarchical organization in the dorsal visual system -- 2.2.3. Hierarchical organization of taste processing -- 2.2.4. Hierarchical organization of olfactory processing -- 2.2.5. Hierarchical multimodal convergence of taste, olfaction, and vision -- 2.2.6. Hierarchical organization of auditory processing -- 2.3. Hierarchical organization of reward value processing -- 2.4. Hierarchical organization of connections to the frontal lobe for short-term memory -- 2.5. Highlights -- 3.1. Hierarchical processing -- 3.2. Short-range neocortical recurrent collaterals -- 3.3. Topographic maps -- 3.4. Modularity -- 3.5. Lateralization of function -- 3.6. Ventral and dorsal cortical areas -- 3.7. Highlights -- 4.1. Introduction -- 4.2. Attractor networks implemented by the recurrent collaterals -- 4.3. Evidence for attractor networks implemented by recurrent collateral connections -- 4.3.1. Short-term Memory -- 4.3.2. Long-term Memory -- 4.3.3. Decision-Making -- 4.4. storage capacity of attractor networks -- 4.5. global attractor network in hippocampal CA3, but local in neocortex -- 4.6. speed of operation of cortical attractor networks -- 4.7. Dilution of recurrent collateral cortical connectivity -- 4.8. Self-organizing topographic maps in the neocortex -- 4.9. Attractors formed by forward and backward connections between cortical areas? -- 4.10. Interacting attractor networks -- 4.11. Highlights -- 5.1. Reasons why the brain is inherently noisy and stochastic -- 5.2. Attractor networks, energy landscapes, and stochastic neurodynamics -- 5.3. multistable system with noise -- 5.4. Stochastic dynamics and the stability of short-term memory -- 5.4.1. Analysis of the stability of short-term memory -- 5.4.2. Stability and noise in a model of short-term memory -- 5.5. Long-term memory recall -- 5.6. Stochastic dynamics and probabilistic decision-making in an attractor network -- 5.6.1. Decision-making in an attractor network -- 5.6.2. Theoretical framework: a probabilistic attractor network -- 5.6.3. Stationary multistability analysis: mean-field -- 5.6.4. Integrate-and-fire simulations of decision-making: spiking dynamics -- 5.6.5. Reaction times of the neuronal responses -- 5.6.6. Percentage correct -- 5.6.7. Finite-size noise effects -- 5.6.8. Comparison with neuronal data during decision-making -- 5.6.9. Testing the model of decision-making with human functional neuroimaging -- 5.6.10. Decisions based on confidence in one's decisions: self-monitoring -- 5.6.11. Decision-making with multiple alternatives -- 5.6.12. matching law -- 5.6.13. Comparison with other models of decision-making -- 5.7. Perceptual decision-making and rivalry -- 5.8. Symmetry-breaking -- 5.9. evolutionary utility of probabilistic choice -- 5.10. Selection between conscious vs unconscious decision-making, and free will -- 5.11. Creative thought -- 5.12. Unpredictable behaviour -- 5.13. Predicting a decision before the evidence is applied -- 5.14. Highlights -- 6.1. Bottom-up attention -- 6.2. Top-down attention -- biased competition -- 6.2.1. biased competition hypothesis -- 6.2.2. Biased competition -- single neuron studies -- 6.2.3. Non-spatial attention -- 6.2.4. Biased competition -- fMRI -- 6.2.5. basic computational module for biased competition -- 6.2.6. Architecture of a model of attention -- 6.2.7. Simulations of basic experimental findings -- 6.2.8. Object recognition and spatial search -- 6.2.9. neuronal and biophysical mechanisms of attention -- 6.2.10. 'Serial' vs 'parallel' attentional processing -- 6.3. Top-down attention -- biased activation -- 6.3.1. Selective attention can selectively activate different cortical areas -- 6.3.2. Sources of the top-down modulation of attention -- 6.3.3. Granger causality used to investigate the source of the top-down biasing -- 6.3.4. Top-down cognitive modulation -- 6.3.5. top-down biased activation model of attention -- 6.4. Conclusions -- 6.5. Highlights -- 7.1. Introduction -- 7.2. Diluted connectivity and the storage capacity of attractor networks -- 7.2.1. autoassociative or attractor network architecture being studied -- 7.2.2. storage capacity of attractor networks with diluted connectivity -- 7.2.3. network simulated -- 7.2.4. effects of diluted connectivity on the capacity of attractor networks -- 7.2.5. Synthesis of the effects of diluted connectivity in attractor networks -- 7.3. effects of dilution on the capacity of pattern association networks -- 7.4. effects of dilution on the performance of competitive networks -- 7.4.1. Competitive Networks -- 7.4.2. Competitive networks without learning but with diluted connectivity -- 7.4.3. Competitive networks with learning and with diluted connectivity -- 7.4.4. Competitive networks with learning and with full (undiluted) connectivity -- 7.4.5. Overview and implications of diluted connectivity in competitive networks -- 7.5. effects of dilution on the noise in attractor networks -- 7.6. Highlights -- 8.1. Types of encoding -- 8.2. Place coding with sparse distributed firing rate representations -- 8.2.1. Reading the code used by single neurons -- 8.2.2. Understanding the code provided by populations of neurons -- 8.3. Synchrony, coherence, and binding -- 8.4. Principles by which the representations are formed -- 8.5. Information encoding in the human cortex -- 8.6. Highlights -- 9.1. Introduction -- 9.2. Associative synaptic modification implemented by long-term potentiation -- 9.3. Forgetting in associative neural networks, and memory reconsolidation -- 9.3.1. Forgetting -- 9.3.2. Factors that influence synaptic modification -- 9.3.3. Recortsolidation -- 9.4. Spike-timing dependent plasticity -- 9.5. Long-term synaptic depression in the cerebellar cortex -- 9.6. Reward prediction error learning -- 9.6.1. Blocking and delta-rule learning -- 9.6.2. Dopamine neuron firing and reward prediction error learning -- 9.7. Highlights -- 10.1. Mechanisms for neuronal adaptation and synaptic depression and facilitation -- 10.1.1. Sodium inactivation leading to neuronal spike-frequency adaptation -- 10.1.2. Calcium activated hyper-polarizing potassium current -- 10.1.3. Short-term synaptic depression and facilitation -- 10.2. Short-term depression of thalamic input to the cortex -- 10.3. Relatively little adaptation in primate cortex when it is operating normally -- 10.4. Acetylcholine, noradrenaline, and other modulators of adaptation and facilitation -- 10.4.1. Acetylcholine -- 10.4.2. Noradrenergic neurons -- 10.5. Synaptic depression and sensory-specific satiety -- 10.6. Neuronal and synaptic adaptation, and the memory for sequential order -- 10.7. Destabilization of short-term memory by adaptation or synaptic depression -- 10.8. Non-reward computation in the orbitofrontal cortex using synaptic depression -- 10.9. Synaptic facilitation and a multiple-item short-term memory -- 10.10. Synaptic facilitation in decision-making -- 10.11. Highlights -- 11.1. Architecture -- 11.2. Learning -- 11.3. Recall -- 11.4. Semantic priming -- 11.5. Top-down Attention -- 11.6. Autoassociative storage, and constraint satisfaction -- 11.7. Highlights -- 12.1. Introduction -- 12.2. Hippocampal circuitry and connections -- 12.3. hippocampus and episodic memory -- 12.4. Autoassociation in the CA3 network for episodic memory -- 12.5. dentate gyrus as a pattern separation mechanism, and neurogenesis -- 12.6. Rodent place cells vs primate spatial view cells -- 12.7. Backprojections, and the recall of information from the hippocampus to neocortex -- 12.8. Subcortical structures connected to the hippocampo-cortical memory system -- 12.9. Highlights -- 13.1. No neurogenesis in the adult neocortex -- 13.2. Limited neurogenesis in the adult hippocampal dentate gyrus -- 13.3. Neurogenesis in the chemosensing receptor systems -- 13.4. Highlights -- 14.1. Hierarchical cortical organization with convergence -- 14.2. Feature combinations -- 14.3. Sparse distributed representations -- 14.4. Self-organization by feedforward processing without a teacher -- 14.5. Learning guided by the statistics of the visual inputs -- 14.6. Bottom up saliency -- 14.7. Lateral interactions shape receptive fields
14.8. Top-down selective attention vs feedforward processing -- 14.9. Topological maps to simplify connectivity -- 14.10. Biologically decodable output representations -- 14.11. Highlights -- 15.1. Emotion, reward value, and their evolutionary adaptive utility -- 15.2. Motivation and reward value -- 15.3. Principles of cortical design for emotion and motivation -- 15.4. Objects are first represented independently of reward value -- 15.5. Specialized systems for face identity and expression processing in primates -- 15.6. Unimodal processing to the object level before multimodal convergence -- 15.7. common scale for reward value -- 15.8. Sensory-specific satiety -- 15.9. Economic value is represented in the orbitofrontal cortex -- 15.10. Neuroeconomics vs classical microeconomics -- 15.11. Output systems influenced by orbitofrontal cortex reward value representations -- 15.12. Decision-making about rewards in the anterior orbitofrontal cortex -- 15.13. Probabilistic emotion-related decision-making -- 15.14. Non-reward, error, neurons in the orbitofrontal cortex -- 15.15. Reward reversal learning in the orbitofrontal cortex -- 15.16. Dopamine neurons and emotion -- 15.17. explicit reasoning system vs the emotional system -- 15.18. Pleasure -- 15.19. Personality relates to differences in sensitivity to rewards and punishers -- 15.20. Highlights -- 16.1. Stochastic noise, attractor dynamics, and schizophrenia -- 16.1.1. Introduction -- 16.1.2. dynamical systems hypothesis of the symptoms of schizophrenia -- 16.1.3. depth of the basins of attraction: mean-field flow analysis -- 16.1.4. Decreased stability produced by reduced NMDA conductances -- 16.1.5. Increased distractibility produced by reduced NMDA conductances -- 16.1.6. Synthesis: network instability and schizophrenia -- 16.2. Stochastic noise, attractor dynamics, and obsessive-compulsive disorder -- 16.2.1. Introduction -- 16.2.2. hypothesis about obsessive-compulsive disorder -- 16.2.3. Glutamate and increased depth of the basins of attraction -- 16.2.4. Synthesis on obsessive-compulsive disorder -- 16.3. Stochastic noise, attractor dynamics, and depression -- 16.3.1. Introduction -- 16.3.2. non-reward attractor theory of depression -- 16.3.3. Evidence consistent with the theory -- 16.3.4. Relation to other brain systems implicated in depression -- 16.3.5. Implications for treatments -- 16.3.6. Mania and bipolar disorder -- 16.4. Stochastic noise, attractor dynamics, and aging -- 16.4.1. NMDA receptor hypofunction -- 16.4.2. Dopamine -- 16.4.3. Impaired synaptic modification -- 16.4.4. Cholinergic function and memory -- 16.5. Highlights -- 17.1. Neurodynamical hypotheses about language and syntax -- 17.1.1. Binding by synchrony? -- 17.1.2. Syntax using a place code -- 17.1.3. Temporal trajectories through a state space of attractors -- 17.1.4. Hypotheses about the implementation of language in the cerebral cortex -- 17.2. Tests of the hypotheses -- a model -- 17.2.1. Attractor networks with stronger forward than backward connections -- 17.2.2. operation of a single attractor network module -- 17.2.3. Spike frequency adaptation mechanism -- 17.3. Tests of the hypotheses -- findings with the model -- 17.3.1. production system -- 17.3.2. decoding system -- 17.4. Evaluation of the hypotheses -- 17.5. Highlights -- 18.1. Introduction -- 18.2. Different types of cerebral neocortex: towards a computational understanding -- 18.2.1. Neocortex or isocortex -- 18.2.2. Olfactory (pyriform) cortex -- 18.2.3. Hippocampal cortex -- 18.3. Addition of areas in the neocortical hierarchy -- 18.4. Evolution of the orbitofrontal cortex -- 18.5. Evolution of the taste and flavour system -- 18.5.1. Principles -- 18.5.2. Taste processing in rodents -- 18.6. Evolution of the temporal lobe cortex -- 18.7. Evolution of the frontal lobe cortex -- 18.8. Highlights -- 19.1. Introduction -- 19.2. Hypotheses about the genes that build cortical neural networks -- 19.3. Genetic selection of neuronal network parameters -- 19.4. Simulation of the evolution of neural networks using a genetic algorithm -- 19.4.1. neural networks -- 19.4.2. specification of the genes -- 19.4.3. genetic algorithm, and general procedure -- 19.4.4. Pattern association networks -- 19.4.5. Autoassociative networks -- 19.4.6. Competitive networks -- 19.5. Evaluation of the gene-based evolution of single-layer networks -- 19.6. gene-based evolution of multi-layer cortical systems -- 19.7. Highlights -- 20.1. Systems-level architecture of the basal ganglia -- 20.2. What computations are performed by the basal ganglia? -- 20.3. How do the basal ganglia perform their computations? -- 20.4. Comparison of selection in the basal ganglia and cerebral cortex -- 20.5. Highlights -- 21.1. Is sleep necessary for cortical function? -- 21.2. Is sleep involved in memory consolidation? -- 21.3. Dreams -- 21.4. Highlights -- 22.1. Introduction -- 22.2. Higher-Order Syntactic Thought (HOST) theory of consciousness -- 22.2.1. Multiple routes to action -- 22.2.2. computational hypothesis of consciousness -- 22.2.3. Adaptive value of processing that is related to consciousness -- 22.2.4. Symbol grounding -- 22.2.5. Qualia -- 22.2.6. Pathways -- 22.2.7. Consciousness and causality -- 22.2.8. Consciousness and higher-order syntactic thoughts -- 22.3. Selection between conscious vs unconscious decision-making systems -- 22.3.1. Dual major routes to action: implicit and explicit -- 22.3.2. Selfish Gene vs The Selfish Phenotype -- 22.3.3. Decision-making between the implicit and explicit systems -- 22.4. Determinism -- 22.5. Free will -- 22.6. Content and meaning in representations -- 22.7. causal role of consciousness and the relation between the mind and the brain -- 22.8. Comparison with other theories of consciousness -- 22.8.1. Higher-order thought theories -- 22.8.2. Oscillations and temporal binding -- 22.8.3. high neural threshold for information to reach consciousness -- 22.8.4. James-Lange theory and Damasio's somatic marker hypothesis -- 22.8.5. LeDoux's approach to emotion and consciousness -- 22.8.6. Panksepp's approach to emotion and consciousness -- 22.8.7. Global workspace theories of consciousness -- 22.8.8. Monitoring and consciousness -- 22.9. Highlights -- 23.1. Introduction -- 23.2. Architecture of the cerebellum -- 23.2.1. connections of the parallel fibres onto the Purkinje cells -- 23.2.2. climbing fibre input to the Purkinje cell -- 23.2.3. mossy fibre to granule cell connectivity -- 23.3. Modifiable synapses of parallel fibres onto Purkinje cell dendrites -- 23.4. cerebellar cortex as a perceptron -- 23.5. Highlights: differences between cerebral and cerebellar cortex microcircuitry -- 24.1. Introduction -- 24.2. Systems-level functions of the hippocampus -- 24.2.1. Systems-level anatomy -- 24.2.2. Evidence from the effects of damage to the hippocampus -- 24.2.3. necessity to recall information from the hippocampus -- 24.2.4. Systems-level neurophysiology of the primate hippocampus -- 24.2.5. Head direction cells in the presubiculum -- 24.2.6. Perirhinal cortex, recognition memory, and long-term familiarity memory -- 24.3. theory of the operation of hippocampal circuitry as a memory system -- 24.3.1. Hippocampal circuitry -- 24.3.2. Entorhinal cortex -- 24.3.3. CA3 as an autoassociation memory -- 24.3.4. Dentate granule cells -- 24.3.5. CA1 cells -- 24.3.6. Recoding in CA1 to facilitate retrieval to the neocortex -- 24.3.7. Backprojections to the neocortex, memory recall, and consolidation -- 24.3.8. Backprojections to the neocortex -- quantitative aspects -- 24.3.9. Simulations of hippocampal operation -- 24.3.10. learning of spatial view and place cell representations -- 24.3.11. Linking the inferior temporal visual cortex to spatial view and place cells -- 24.3.12. scientific theory of the art of memory: scientia artis memoriae -- 24.4. Tests of the theory of hippocampal cortex operation -- 24.4.1. Dentate gyrus (DG) subregion of the hippocampus -- 24.4.2. CA3 subregion of the hippocampus -- 24.4.3. CA1 subregion of the hippocampus -- 24.5. Evaluation of the theory of hippocampal cortex operation -- 24.5.1. Tests of the theory by hippocampal system subregion analyses -- 24.5.2. Comparison with other theories of hippocampal function -- 24.6. Highlights -- 25.1. Introduction -- 25.2. Invariant representations of faces and objects in the inferior temporal visual cortex -- 25.2.1. Processing to the inferior temporal cortex in the primate visual system -- 25.2.2. Translation invariance and receptive field size -- 25.2.3. Reduced translation invariance in natural scenes -- 25.2.4. Size and spatial frequency invariance -- 25.2.5. Combinations of features in the correct spatial configuration -- 25.2.6. view-invariant representation -- 25.2.7. Learning in the inferior temporal cortex -- 25.2.8. Distributed encoding -- 25.2.9. Face expression, gesture, and view -- 25.2.10. Specialized regions in the temporal cortical visual areas -- 25.3. Approaches to invariant object recognition -- 25.3.1. Feature spaces -- 25.3.2. Structural descriptions and syntactic pattern recognition -- 25.3.3. Template matching and the alignment approach -- 25.3.4. Invertible networks that can reconstruct their inputs -- 25.3.5. Feature hierarchies -- 25.4. Hypotheses about object recognition mechanisms -- 25.5. Computational issues in feature hierarchies
25.5.1. architecture of VisNet -- 25.5.2. Initial experiments with VisNet -- 25.5.3. optimal parameters for the temporal trace used in the learning rule -- 25.5.4. Different forms of the trace learning rule, and error correction -- 25.5.5. issue of feature binding, and a solution -- 25.5.6. Operation in a cluttered environment -- 25.5.7. Learning 3D transforms -- 25.5.8. Capacity of the architecture, and an attractor implementation -- 25.5.9. Vision in natural scenes -- effects of background versus attention -- 25.5.10. representation of multiple objects in a scene -- 25.5.11. Learning invariant representations using spatial continuity -- 25.5.12. Lighting invariance -- 25.5.13. Invariant global motion in the dorsal visual system -- 25.5.14. Deformation-invariant object recognition -- 25.5.15. Learning invariant representations of scenes and places -- 25.5.16. Finding and recognising objects in natural scenes -- 25.6. Further approaches to invariant object recognition -- 25.6.1. Other types of slow learning -- 25.6.2. HMAX -- 25.6.3. Sigma-Pi synapses -- 25.6.4. Deep learning -- 25.7. Visuo-spatial scratchpad memory, and change blindness -- 25.8. Processes involved in object identification -- 25.9. Highlights -- 26.1. Principles of cortical operation, not a single theory -- 26.2. Levels of explanation, and the mind-brain problem -- 26.3. Brain computation compared to computation on a digital computer -- 26.4. Understanding how the brain works -- 26.5. Synthesis on principles of operation of the cerebral cortex -- 26.5.1. Hierarchical organization -- 26.5.2. Localization of function -- 26.5.3. Recurrent collaterals and attractor networks -- 26.5.4. noisy cortex -- 26.5.5. Top-down attention -- 26.5.6. Diluted connectivity -- 26.5.7. Sparse distributed graded firing rate encoding -- 26.5.8. Synaptic modification -- 26.5.9. Adaptation and facilitation -- 26.5.10. Backprojections -- 26.5.11. Neurogenesis -- 26.5.12. Binding and syntax -- 26.5.13. Evolution of the cerebral cortex -- 26.5.14. Genetic specification of cortical design -- 26.5.15. cortical systems for emotion -- 26.5.16. Memory systems -- 26.5.17. Visual cortical processing for invariant visual object recognition -- 26.5.18. Cortical lamination, operation, and evolution -- 26.6. Highlights -- A.1. Vectors -- A.1.1. inner or dot product of two vectors -- A.1.2. length of a vector -- A.1.3. Normalizing the length of a vector -- A.1.4. angle between two vectors: the normalized dot product -- A.1.5. outer product of two vectors -- A.1.6. Linear and non-linear systems -- A.1.7. Linear combinations, linear independence, and linear separability -- A.2. Application to understanding simple neural networks -- A.2.1. Capability and limitations of single-layer networks -- A.2.2. Non-linear networks: neurons with non-linear activation functions -- A.2.3. Non-linear networks: neurons with non-linear activations -- B.1. Introduction -- B.2. Pattern association memory -- B.2.1. Architecture and operation -- B.2.2. simple model -- B.2.3. vector interpretation -- B.2.4. Properties -- B.2.5. Prototype extraction, extraction of central tendency, and noise reduction -- B.2.6. Speed -- B.2.7. Local learning rule -- B.2.8. Implications of different types of coding for storage in pattern associators -- B.3. Autoassociation or attractor memory -- B.3.1. Architecture and operation -- B.3.2. Introduction to the analysis of the operation of autoassociation networks -- B.3.3. Properties -- B.3.4. Use of autoassociation networks in the brain -- B.4. Competitive networks, including self-organizing maps -- B.4.1. Function -- B.4.2. Architecture and algorithm -- B.4.3. Properties -- B.4.4. Utility of competitive networks in information processing by the brain -- B.4.5. Guidance of competitive learning -- B.4.6. Topographic map formation -- B.4.7. Invariance learning by competitive networks -- B.4.8. Radial Basis Function networks -- 8.4.9. Further details of the algorithms used in competitive networks -- B.5. Continuous attractor networks -- 8.5.1. Introduction -- B.5.2. generic model of a continuous attractor network -- B.5.3. Learning the synaptic strengths in a continuous attractor network -- B.5.4. capacity of a continuous attractor network: multiple charts -- B.5.5. Continuous attractor models: path integration -- B.5.6. Stabilization of the activity packet within a continuous attractor network -- B.5.7. Continuous attractor networks in two or more dimensions -- B.5.8. Mixed continuous and discrete attractor networks -- B.6. Network dynamics: the integrate-and-fire approach -- B.6.1. From discrete to continuous time -- B.6.2. Continuous dynamics with discontinuities -- B.6.3. integrate-and-fire implementation -- B.6.4. speed of processing of attractor networks -- B.6.5. speed of processing of a four-layer hierarchical network -- 8.6.6. Spike response model -- B.7. Network dynamics: introduction to the mean-field approach -- B.8. Mean-field based neurodynamics -- 8.8.1. Population activity -- 8.8.2. mean-field approach used in a model of decision-making -- 8.8.3. model parameters used in the mean-field analyses of decision-making -- 8.8.4. basic computational module based on biased competition -- B.8.5. Multimodular neurodynamical architectures -- B.9. Sequence memory implemented by adaptation in an attractor network -- B.10. Error correction networks -- B.10.1. Architecture and general description -- B.10.2. Generic algorithm for a one-layer error correction network -- B.10.3. Capability and limitations of single-layer error-correcting networks -- B.10.4. Properties -- B.11. Error backpropagation multilayer networks -- B.11.1. Introduction -- B.11.2. Architecture and algorithm -- 8.11.3. Properties of multilayer networks trained by error backpropagation -- B.12. Biologically plausible networks vs backpropagation -- B.13. Convolution networks -- B.14. Contrastive Hebbian learning: the Boltzmann machine -- B.15. Deep Belief Networks -- B.16. Reinforcement learning -- B.16.1. Associative reward-penalty algorithm of Barto and Sutton -- B.16.2. Reward prediction error or delta rule learning, and classical conditioning -- B.16.3. Temporal Difference (TD) learning -- 8.17. Highlights -- C.1. Information theory -- C.1.1. information conveyed by definite statements -- C.1.2. Information conveyed by probabilistic statements -- C.1.3. Information sources, information channels, and information measures -- C.1.4. information carried by a neuronal response and its averages -- C.1.5. information conveyed by continuous variables -- C.2. information carried by neuronal responses -- C.2.1. limited sampling problem -- C.2.2. Correction procedures for limited sampling -- C.2.3. information from multiple cells: decoding procedures -- C.2.4. Information in the correlations between cells: a decoding approach -- C.2.5. Information in the correlations between cells: second derivative approach -- C.3. Information theory results -- C.3.1. sparseness of the distributed encoding used by the brain -- C.3.2. information from single neurons -- C.3.3. information from single neurons: temporal codes versus rate codes -- C.3.4. information from single neurons: the speed of information transfer -- C.3.5. information from multiple cells: independence versus redundancy -- C.3.6. Should one neuron be as discriminative as the whole organism? -- C.3.7. information from multiple cells: the effects of cross-correlations -- C.3.8. Conclusions on cortical neuronal encoding -- C.4. Information theory terms -- a short glossary -- C.5. Highlights -- D.1. Introduction -- D.2. Autoassociation or attractor networks -- D.2.1. Running the simulation -- D.2.2. Exercises -- D.3. Pattern association networks -- D.3.1. Running the simulation -- D.3.2. Exercises -- D.4. Competitive networks and Self-Organizing Maps -- D.4.1. Running the simulation -- D.4.2. Exercises -- D.5. Highlights
Description based on print version record
Link Original 9780198784852 0198784856 (DLC) 2016944945 (OCoLC)953597754
Subject Cerebral cortex
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