Atjaunināt sīkdatņu piekrišanu

E-grāmata: Brain Computations: What and How

4.00/5 (2 ratings by Goodreads)
(Professor in the Department of Computer Science, University of Warwick and Oxford Centre for Computational Neuroscience)
  • Formāts: 944 pages
  • Izdošanas datums: 08-Dec-2020
  • Izdevniecība: Oxford University Press
  • Valoda: eng
  • ISBN-13: 9780192644473
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 65,75 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Brain Computations: What and HowPalielināt
  • Formāts: 944 pages
  • Izdošanas datums: 08-Dec-2020
  • Izdevniecība: Oxford University Press
  • Valoda: eng
  • ISBN-13: 9780192644473
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

In order to understand how the brain works, it is essential to know what is computed by different brain systems, and how those computations are performed.

Brain Computations: What and How elucidates what is computed in different brain systems and describes current computational approaches and models of how each of these brain systems computes.

This approach has enormous potential for helping us understand ourselves better in health. Potential applications of this understanding are to the treatment of the brain in disease, as well as to artificial intelligence, which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.

Pioneering in its approach, Brain Computations: What and How will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics.

Recenzijas

This neuronal network approach stands in contrast to connectionist approaches and also focuses exclusively on higher primate and human modeling. Helpful chapter highlights and several practical appendixes are provided, and the bibliography is excellent. * H. Storl, Augustana College (IL), CHOICE * This "bottom-up" approach to data-driven neuroscientific discovery serves as the perfect primer for those who study brain sciences, cognitive sciences, artificial intelligence, neuro-engineering, neuropsychology, and empirically oriented philosophy. * H. Storl, CHOICE * Brain Computations is the first complete attempt to summarize our current knowledge about computation in the brain, at a level a graduate can understand. ... This is a biologically grounded, full systems neuroscience textbook-which makes it one of a kind. ... Hippocampal memories, action selection in the striatum, orbitofrontal reward representations, emotion in the limbic system, cerebellar motor control, parietal spatial coordinate transforms, place fields, and posterior visual object recognition-all these can emerge from relatively simple rules. This is Rolls' unspoken but substantial grand unifying theory. (full review https://doi.org/10.1093/brain/awab477) * Brain * He concludes with 13 principles about how information in encoded in neural networks. This is almost the Holy Grail of neuroscience, the language of neurons, what makes us what we are. Yet, these ideas are presented in a simple unassuming scientific language ... * Nikolaos C. Aggelopoulos, Neurosurgery *

1 Introduction
1(39)
1.1 What and how the brain computes: introduction
1(2)
1.2 What and how the brain computes: plan of the book
3(2)
1.3 Neurons
5(1)
1.4 Neurons in a network
6(2)
1.5 Synaptic modification
8(2)
1.6 Long-term potentiation and long-term depression
10(4)
1.7 Information encoding by neurons, and distributed representations
14(4)
1.7.1 Definitions
16(1)
1.7.2 Advantages of different types of coding
16(2)
1.8 Neuronal network approaches versus connectionism
18(1)
1.9 Introduction to three neuronal network architectures
18(3)
1.10 Systems-level analysis of brain function
21(2)
1.11 Brodmann areas
23(3)
1.12 The fine structure of the cerebral neocortex
26(14)
1.12.1 The fine structure and connectivity of the neocortex
27(1)
1.12.2 Excitatory cells and connections
27(2)
1.12.3 Inhibitory cells and connections
29(3)
1.12.4 Quantitative aspects of cortical architecture
32(2)
1.12.5 Functional pathways through the cortical layers
34(4)
1.12.6 The scale of lateral excitatory and inhibitory effects, and modules
38(2)
2 The ventral visual system
40(136)
2.1 Introduction and overview
40(7)
2.1.1 Introduction
40(1)
2.1.2 Overview of what is computed in the ventral visual system
40(3)
2.1.3 Overview of how computations are performed in the ventral visual system
43(2)
2.1.4 What is computed in the ventral visual system is unimodal, and is related to other `what' systems after the inferior temporal visual cortex
45(2)
2.2 What: V1 -- primary visual cortex
47(1)
2.3 What: V2 and V4 -- intermediate processing areas in the ventral visual system
48(1)
2.4 What: Invariant representations of faces and objects in the inferior temporal visual cortex
49(22)
2.4.1 Reward value is not represented in the ventral visual system
49(1)
2.4.2 Translation invariant representations
50(1)
2.4.3 Reduced translation invariance in natural scenes
50(3)
2.4.4 Size and spatial frequency invariance
53(1)
2.4.5 Combinations of features in the correct spatial configuration
54(1)
2.4.6 A view-invariant representation
54(4)
2.4.7 Learning in the inferior temporal cortex
58(2)
2.4.8 A sparse distributed representation is what is computed in the ventral visual system
60(6)
2.4.9 Face expression, gesture, and view
66(1)
2.4.10 Specialized regions in the temporal cortical visual areas
66(5)
2.5 How the computations are performed: approaches to invariant object recognition
71(11)
2.5.1 Feature spaces
72(1)
2.5.2 Structural descriptions and syntactic pattern recognition
73(2)
2.5.3 Template matching and the alignment approach
75(1)
2.5.4 Invertible networks that can reconstruct their inputs
76(1)
2.5.5 Deep learning
76(1)
2.5.6 Feature hierarchies
77(5)
2.6 Hypotheses about how the computations are performed in a feature hierarchy approach
82(4)
2.7 VisNet: a model of how the computations are performed in the ventral visual system
86(75)
2.7.1 The architecture of VisNet
87(9)
2.7.2 Initial experiments with VisNet
96(6)
2.7.3 The optimal parameters for the temporal trace used in the learning rule
102(2)
2.7.4 Different forms of the trace learning rule, and error correction
104(8)
2.7.5 The issue of feature binding, and a solution
112(13)
2.7.6 Operation in a cluttered environment
125(7)
2.7.7 Learning 3D transforms
132(5)
2.7.8 Capacity of the architecture, and an attractor implementation
137(3)
2.7.9 Vision in natural scenes -- effects of background versus attention
140(8)
2.7.10 The representation of multiple objects in a scene
148(2)
2.7.11 Learning invariant representations using spatial continuity
150(1)
2.7.12 Lighting invariance
151(2)
2.7.13 Deformation-invariant object recognition
153(1)
2.7.14 Learning invariant representations of scenes and places
154(2)
2.7.15 Finding and recognising objects in natural scenes
156(3)
2.7.16 Non-accidental properties, and transform invariant object recognition
159(2)
2.8 Further approaches to invariant object recognition
161(6)
2.8.1 Other types of slow learning
161(1)
2.8.2 HMAX
161(5)
2.8.3 Hierarchical convolutional deep neural networks
166(1)
2.8.4 Sigma-Pi synapses
167(1)
2.9 Visuo-spatial scratchpad memory, and change blindness
167(2)
2.10 Processes involved in object identification
169(1)
2.11 Top-down attentional modulation is implemented by biased competition
170(3)
2.12 Highlights on how the computations are performed in the ventral visual system
173(3)
3 The dorsal visual system
176(16)
3.1 Introduction, and overview of the dorsal cortical visual stream
176(1)
3.2 Global motion in the dorsal visual system
177(2)
3.3 Invariant object-based motion in the dorsal visual system
179(2)
3.4 What is computed in the dorsal visual system: visual coordinate transforms
181(4)
3.4.1 The transform from retinal to head-based coordinates
182(1)
3.4.2 The transform from head-based to allocentric bearing coordinates
183(1)
3.4.3 A transform from allocentric bearing coordinates to allocentric spatial view coordinates
184(1)
3.5 How visual coordinate transforms are computed in the dorsal visual system
185(7)
3.5.1 Gain modulation
185(1)
3.5.2 Mechanisms of gain modulation using a trace learning rule
186(1)
3.5.3 Gain modulation by eye position to produce a head-centered representation in Layer 1 of VisNetCT
187(1)
3.5.4 Gain modulation by head direction to produce an allocentric bearing to a landmark in Layer 2 of VisNetCT
188(1)
3.5.5 Gain modulation by place to produce an allocentric spatial view representation in Layer 3 of VisNetCT
189(1)
3.5.6 The utility of the coordinate transforms in the dorsal visual system
190(2)
4 The taste and flavour system
192(25)
4.1 Introduction and overview
192(2)
4.1.1 Introduction
192(1)
4.1.2 Overview of what is computed in the taste and flavour system
192(2)
4.1.3 Overview of how computations are performed in the taste and flavour system
194(1)
4.2 Taste and related pathways: what is computed
194(18)
4.2.1 Hierarchically organised anatomical pathways
194(3)
4.2.2 Taste neuronal tuning become more selective through the taste hierarchy
197(1)
4.2.3 The primary, insular, taste cortex represents what taste is present and its intensity
198(1)
4.2.4 The secondary, orbitofrontal, taste cortex, and its representation of the reward value and pleasantness of taste
199(2)
4.2.5 Sensory-specific satiety is computed in the orbitofrontal cortex
201(3)
4.2.6 Oral texture is represented in the primary and secondary taste cortex: viscosity and fat texture
204(3)
4.2.7 Vision and olfaction converge using associative learning with taste to represent flavour in the secondary but not primary taste cortex
207(1)
4.2.8 Top-down attention and cognition can modulate taste and flavour representations in the taste cortical areas
208(2)
4.2.9 The tertiary taste cortex in the anterior cingulate cortex provides the rewards for action-reward learning
210(2)
4.3 Taste and related pathways: how the computations are performed
212(5)
4.3.1 Increased selectivity of taste and flavor neurons through the hierarchy by competitive learning and convergence
212(1)
4.3.2 Pattern association learning of associations of visual and olfactory stimuli with taste
212(1)
4.3.3 Rule-based reversal of visual to taste associations in the orbitofrontal cortex
212(1)
4.3.4 Sensory-specific satiety is implemented by adaptation of synapses onto orbitofrontal cortex neurons
212(1)
4.3.5 Top-down cognitive and attentional modulation is implemented by biased activation
213(4)
5 The olfactory system
217(15)
5.1 Introduction
217(2)
5.1.1 Overview of what is computed in the olfactory system
217(1)
5.1.2 Overview of how the computations are performed in the olfactory system
218(1)
5.2 What is computed in the olfactory system
219(7)
5.2.1 1000 gene-encoded olfactory receptor types, and 1000 corresponding glomerulus types in the olfactory bulb
219(2)
5.2.2 The primary olfactory, pyriform, cortex: olfactory feature combinations are what is represented
221(1)
5.2.3 Orbitofrontal cortex: olfactory neuronal response selectivity
221(1)
5.2.4 Orbitofrontal cortex: olfactory to taste convergence
222(1)
5.2.5 Orbitofrontal cortex: olfactory to taste association learning and reversal
223(2)
5.2.6 Orbitofrontal cortex: olfactory reward value is represented
225(1)
5.3 How computations are performed in the olfactory system
226(6)
5.3.1 Olfactory receptors, and the olfactory bulb
226(1)
5.3.2 Olfactory (pyriform) cortex
227(4)
5.3.3 Orbitofrontal cortex
231(1)
6 The somatosensory system
232(12)
6.1 What is computed in the somatosensory system
232(10)
6.1.1 The receptors and periphery
232(1)
6.1.2 The anterior somatosensory cortex, areas 1, 2, 3a, and 3b, in the anterior parietal cortex
232(1)
6.1.3 The ventral somatosensory stream: areas S2 and PV, in the lateral parietal cortex
233(1)
6.1.4 The dorsal somatosensory stream to area 5 and then 7b, in the posterior parietal cortex
234(2)
6.1.5 Somatosensory representations in the insula
236(1)
6.1.6 Somatosensory and temperature inputs to the orbitofrontal cortex, affective value, pleasant touch, and pain
236(4)
6.1.7 Decision-making in the somatosensory system
240(2)
6.2 How computations are performed in the somatosensory system
242(2)
6.2.1 Hierarchical computation in the somatosensory system
242(1)
6.2.2 Computations for pleasant touch and pain
243(1)
6.2.3 The mechanisms for somatosensory decision-making
243(1)
7 The auditory system
244(9)
7.1 Introduction, and overview of computations in the auditory system
244(1)
7.2 Auditory Localization
245(3)
7.3 Ventral and dorsal cortical auditory pathways
248(1)
7.4 The ventral cortical auditory stream
249(2)
7.5 The dorsal cortical auditory stream
251(1)
7.6 How the computations are performed in the auditory system
251(2)
8 The temporal cortex
253(7)
8.1 Introduction and overview
253(1)
8.2 Middle temporal gyrus and face expression and gesture
253(2)
8.3 Semantic representations in the temporal lobe neocortex
255(4)
8.3.1 Neurophysiology
255(1)
8.3.2 Neuropsychology
256(1)
8.3.3 Functional neuroimaging
256(1)
8.3.4 Brain stimulation
257(2)
8.4 The mechanisms for semantic learning in the human anterior temporal lobe
259(1)
9 The hippocampus, memory, and spatial function
260(103)
9.1 Introduction and overview
260(3)
9.1.1 Overview of what is computed by the hippocampal system
260(2)
9.1.2 Overview of how the computations are performed by the hippocampal system
262(1)
9.2 What is computed in the hippocampus
263(30)
9.2.1 Systems-level anatomy
263(2)
9.2.2 Evidence from the effects of damage to the hippocampus
265(2)
9.2.3 Episodic memories need to be recalled from the hippocampus, and can be used to help build neocortical semantic memories
267(3)
9.2.4 Systems-level neurophysiology of the primate hippocampus
270(15)
9.2.5 Head direction cells in the presubiculum
285(1)
9.2.6 Perirhinal cortex, recognition memory, and long-term familiarity memory
286(7)
9.3 How computations are performed in the hippocampal system
293(51)
9.3.1 Historical development of the theory of the hippocampus
293(3)
9.3.2 Hippocampal circuitry
296(1)
9.3.3 Medial entorhinal cortex, spatial processing streams, and grid cells
297(4)
9.3.4 Lateral entorhinal cortex, object processing streams, and the generation of time cells in the hippocampus
301(6)
9.3.5 CA3 as an autoassociation memory
307(18)
9.3.6 Dentate granule cells
325(3)
9.3.7 CA1 cells
328(5)
9.3.8 Backprojections to the neocortex, memory recall, and consolidation
333(3)
9.3.9 Backprojections to the neocortex -- quantitative aspects
336(3)
9.3.10 Simulations of hippocampal operation
339(2)
9.3.11 The learning of spatial view and place cell representations
341(1)
9.3.12 Linking the inferior temporal visual cortex to spatial view and place cells
342(2)
9.3.13 A scientific theory of the art of memory: scientia artis memoriae
344(1)
9.4 Tests of the theory of hippocampal cortex operation
344(14)
9.4.1 Dentate gyrus (DG) subregion of the hippocampus
345(3)
9.4.2 CA3 subregion of the hippocampus
348(7)
9.4.3 CA1 subregion of the hippocampus
355(3)
9.5 Comparison with other theories of hippocampal function
358(5)
10 The parietal cortex, spatial functions, and navigation
363(16)
10.1 Introduction and overview
363(2)
10.1.1 Introduction
363(1)
10.1.2 Overview of what is computed in the parietal cortex
363(1)
10.1.3 Overview of how the computations are performed in the parietal cortex
364(1)
10.2 Precuneus and medial area 7
365(1)
10.3 Navigation: What computations are performed in the parietal and related cortex
366(1)
10.4 How navigation is performed
367(12)
10.4.1 Navigation using a hippocampal allocentric Euclidean cognitive map
367(1)
10.4.2 Navigation using an entorhinal cortex goal vector system
367(1)
10.4.3 Transforms between allocentric and egocentric representations
368(3)
10.4.4 Navigational computations using neuron types found in primates
371(8)
11 The orbitofrontal cortex, amygdala, reward value, and emotion
379(68)
11.1 Introduction and overview
379(4)
11.1.1 Introduction
379(1)
11.1.2 Overview of what is computed in the orbitofrontal cortex
379(2)
11.1.3 Overview of how the computations are performed by the orbitofrontal cortex
381(2)
11.2 The topology and connections of the orbitofrontal cortex
383(4)
11.2.1 Inputs to the orbitofrontal cortex
383(3)
11.2.2 Outputs of the orbitofrontal cortex
386(1)
11.3 What is computed in the orbitofrontal cortex
387(41)
11.3.1 The orbitofrontal cortex represents reward value
387(5)
11.3.2 Neuroeconomic value is represented in the orbitofrontal cortex
392(4)
11.3.3 A representation of face and voice expression and other socially relevant stimuli in the orbitofrontal cortex
396(3)
11.3.4 Negative reward prediction error neurons in the orbitofrontal cortex
399(4)
11.3.5 The human medial orbitofrontal cortex represents rewards, and the lateral orbitofrontal cortex non-reward and punishers
403(3)
11.3.6 Decision-making in the orbitofrontal / ventromedial prefrontal cortex
406(2)
11.3.7 The ventromedial prefrontal cortex and memory
408(1)
11.3.8 The orbitofrontal cortex and emotion
409(2)
11.3.9 Emotional orbitofrontal vs rational routes to action
411(8)
11.3.10 Comparison between the functions of the orbitofrontal cortex and amygdala in emotion
419(9)
11.4 How the computations are performed in the orbitofrontal cortex
428(16)
11.4.1 Decision-making in attractor networks in the brain
429(4)
11.4.2 Analyses of reward-related decision-making mechanisms in the orbitofrontal cortex
433(5)
11.4.3 A model for reversal learning in the orbitofrontal cortex
438(5)
11.4.4 A theory and model of non-reward neural mechanisms in the orbitofrontal cortex
443(1)
11.5 Highlights: the special computational roles of the orbitofrontal cortex
444(3)
12 The cingulate cortex
447(17)
12.1 Introduction to and overview of the cingulate cortex
447(3)
12.1.1 Introduction
447(1)
12.1.2 Overview of what is computed in the cingulate cortex
447(2)
12.1.3 Overview of how the computations are performed by the cingulate cortex
449(1)
12.2 Anterior Cingulate Cortex
450(7)
12.2.1 Anterior cingulate cortex anatomy and connections
450(1)
12.2.2 Anterior cingulate cortex: A framework
451(2)
12.2.3 Pregenual anterior cingulate representations of reward value, and supracallosal anterior cingulate representations of punishers and non-reward
453(2)
12.2.4 Anterior cingulate cortex and action-outcome representations
455(1)
12.2.5 Anterior cingulate cortex lesion effects
455(1)
12.2.6 Subgenual cingulate cortex
456(1)
12.3 Mid-cingulate cortex, the cingulate motor area, and action-outcome learning
457(1)
12.4 The posterior cingulate cortex
458(1)
12.5 How the computations are performed by the cingulate cortex
459(2)
12.5.1 The anterior cingulate cortex and emotion
459(1)
12.5.2 Action-outcome learning
459(1)
12.5.3 Connectivity of the posterior cingulate cortex with the hippocampal memory system
460(1)
12.6 Synthesis and conclusions
461(3)
13 The motor cortical areas
464(4)
13.1 Introduction and overview
464(1)
13.2 What is computed in different cortical motor-related areas
464(2)
13.2.1 Ventral parietal and ventral premotor cortex F4
464(2)
13.2.2 Superior parietal areas with activity related to reaching
466(1)
13.2.3 Inferior parietal areas with activity related to grasping, and ventral premotor cortex F5
466(1)
13.3 The mirror neuron system
466(1)
13.4 How the computations are performed in motor cortical and related areas
467(1)
14 The basal ganglia
468(29)
14.1 Introduction and overview
468(1)
14.2 Systems-level architecture of the basal ganglia
469(2)
14.3 What computations are performed by the basal ganglia?
471(14)
14.3.1 Effects of striatal lesions
471(2)
14.3.2 Neuronal activity in different parts of the striatum
473(12)
14.4 How do the basal ganglia perform their computations?
485(9)
14.4.1 Interaction between neurons and selection of output
485(4)
14.4.2 Convergence within the basal ganglia, useful for stimulus-response habit learning
489(2)
14.4.3 Dopamine as a reward prediction error signal for reinforcement learning in the striatum
491(3)
14.5 Comparison of computations for selection in the basal ganglia and cerebral cortex
494(3)
15 Cerebellar cortex
497(8)
15.1 Introduction
497(1)
15.2 Architecture of the cerebellum
498(4)
15.2.1 The connections of the parallel fibres onto the Purkinje cells
498(1)
15.2.2 The climbing fibre input to the Purkinje cell
499(1)
15.2.3 The mossy fibre to granule cell connectivity
500(2)
15.3 Modifiable synapses of parallel fibres onto Purkinje cell dendrites
502(1)
15.4 The cerebellar cortex as a perceptron
502(1)
15.5 Highlights: differences between cerebral and cerebellar cortex microcircuitry
503(2)
16 The prefrontal cortex
505(22)
16.1 Introduction and overview
505(3)
16.2 Divisions of the lateral prefrontal cortex
508(4)
16.2.1 The dorsolateral prefrontal cortex
509(1)
16.2.2 The caudal prefrontal cortex
510(2)
16.2.3 The ventrolateral prefrontal cortex
512(1)
16.3 The lateral prefrontal cortex and top-down attention
512(3)
16.4 How the computations are performed in the prefrontal cortex
515(12)
16.4.1 Cortical short-term memory systems and attractor networks
515(2)
16.4.2 Prefrontal cortex short-term memory networks, and their relation to perceptual networks
517(5)
16.4.3 Mapping from one representation to another in short-term memory
522(2)
16.4.4 The mechanisms of top-down attention
524(1)
16.4.5 Computational necessity for a separate, prefrontal cortex, short-term memory system
525(1)
16.4.6 Synaptic modification is needed to set up but not to reuse short-term memory systems
525(1)
16.4.7 Sequence memory
525(1)
16.4.8 Working memory, and planning
526(1)
17 Language and syntax in the brain
527(27)
17.1 Introduction and overview
527(2)
17.1.1 Introduction
527(1)
17.1.2 Overview
527(2)
17.2 What is computed in different brain systems to implement language
529(5)
17.2.1 The Wernicke-Lichtheim-Geschwind hypothesis
529(1)
17.2.2 The dual-stream hypothesis of speech comprehension
529(1)
17.2.3 Reading requires different brain systems to hearing speech
530(1)
17.2.4 Semantic representations
531(2)
17.2.5 Syntactic processing
533(1)
17.2.6 The parietal cortex: supramarginal and angular gyri
533(1)
17.3 Hypotheses about how semantic representations are computed
534(1)
17.4 A neurodynamical hypothesis about how syntax is computed
535(19)
17.4.1 Binding by synchrony?
535(1)
17.4.2 Syntax using a place code
536(1)
17.4.3 Temporal trajectories through a state space of attractors
536(1)
17.4.4 Hypotheses about the implementation of language in the cerebral cortex
537(3)
17.4.5 Tests of the hypotheses -- a model
540(5)
17.4.6 Tests of the hypotheses -- findings with the model
545(3)
17.4.7 Evaluation of the hypotheses
548(4)
17.4.8 Further approaches
552(2)
18 Cortical attractor dynamics and connectivity, stochasticity, psychiatric disorders, and aging
554(55)
18.1 Introduction and overview
554(1)
18.1.1 Introduction
554(1)
18.1.2 Overview
554(1)
18.2 The noisy cortex
555(18)
18.2.1 Reasons why the brain is inherently noisy and stochastic
556(3)
18.2.2 Attractor networks, energy landscapes, and stochastic neurodynamics
559(5)
18.2.3 A multistage system with noise
564(2)
18.2.4 Stochastic dynamics and the stability of short-term memory
566(4)
18.2.5 Stochastic dynamics in decision-making, and the evolutionary utility of probabilistic choice
570(1)
18.2.6 Selection between conscious vs unconscious decision-making, and free will
571(1)
18.2.7 Stochastic dynamics and creative thought
572(1)
18.2.8 Stochastic dynamics and unpredictable behaviour
573(1)
18.3 Attractor dynamics and schizophrenia
573(9)
18.3.1 Introduction
573(1)
18.3.2 A dynamical systems hypothesis of the symptoms of schizophrenia
574(3)
18.3.3 Reduced functional connectivity of some brain regions in schizophrenia
577(1)
18.3.4 Beyond the disconnectivity hypothesis of schizophrenia: reduced forward but not backward connectivity
578(4)
18.4 Attractor dynamics and obsessive-compulsive disorder
582(4)
18.4.1 Introduction
582(1)
18.4.2 A hypothesis about obsessive-compulsive disorder
582(3)
18.4.3 Glutamate and increased depth of the basins of attraction
585(1)
18.5 Depression and attractor dynamics
586(14)
18.5.1 Introduction
586(1)
18.5.2 A non-reward attractor theory of depression
587(1)
18.5.3 The orbitofrontal cortex, and the theory of depression
588(2)
18.5.4 Altered connectivity of the orbitofrontal cortex in depression
590(5)
18.5.5 Activations of the orbitofrontal cortex related to depression
595(1)
18.5.6 Implications, and possible treatments, and subtypes of depression
595(3)
18.5.7 Mania and bipolar disorder
598(2)
18.6 Attractor stochastic dynamics, aging, and memory
600(7)
18.6.1 NMDA receptor hypofunction
600(2)
18.6.2 Dopamine and norepinephrine
602(1)
18.6.3 Impaired synaptic modification
602(1)
18.6.4 Cholinergic function and memory
603(4)
18.7 High blood pressure, reduced hippocampal functional connectivity, and impaired memory
607(1)
18.8 Brain development, and structural differences in the brain
608(1)
19 Computations by different types of brain, and by artificial neural systems
609(25)
19.1 Introduction and overview
609(1)
19.2 Computations that combine different computational systems in the brain to produce behaviour
610(1)
19.3 Brain computation compared to computation on a digital computer
610(6)
19.4 Brain computation compared with artificial deep learning networks
616(2)
19.5 Reinforcement Learning
618(2)
19.6 Levels of explanation, and the mind-brain problem
620(2)
19.7 Levels of explanation, and levels of investigation
622(1)
19.8 Brain-Inspired Intelligence
623(1)
19.9 Brain-Inspired Medicine
624(4)
19.9.1 Computational psychiatry and neurology
624(1)
19.9.2 Reward systems in the brain, and their application to understanding food intake control and obesity
625(3)
19.9.3 Multiple Routes to Action
628(1)
19.10 Primates including humans have different brain organisation than rodents
628(6)
19.10.1 The visual system
628(1)
19.10.2 The taste system
629(1)
19.10.3 The olfactory system
629(1)
19.10.4 The somatosensory system
630(1)
19.10.5 The auditory system
630(1)
19.10.6 The hippocampal system and memory
630(1)
19.10.7 The orbitofrontal cortex and amygdala
631(1)
19.10.8 The cingulate cortex
632(1)
19.10.9 The motor system
632(1)
19.10.10 Language
633(1)
A Introduction to linear algebra for neural networks
634(12)
A.1 Vectors
634(6)
A.1.1 The inner or dot product of two vectors
634(2)
A.1.2 The length of a vector
636(1)
A.1.3 Normalizing the length of a vector
636(1)
A.1.4 The angle between two vectors: the normalized dot product
636(1)
A.1.5 The outer product of two vectors
637(1)
A.1.6 Linear and non-linear systems
638(1)
A.1.7 Linear combinations, linear independence, and linear separability
639(1)
A.2 Application to understanding simple neural networks
640(6)
A.2.1 Capability and limitations of single-layer networks
641(2)
A.2.2 Non-linear networks: neurons with non-linear activation functions
643(1)
A.2.3 Non-linear networks: neurons with non-linear activations
644(2)
B Neuronal network models
646(128)
B.1 Introduction
646(1)
B.2 Pattern association memory
646(17)
B.2.1 Architecture and operation
647(2)
B.2.2 A simple model
649(3)
B.2.3 The vector interpretation
652(1)
B.2.4 Properties
653(3)
B.2.5 Prototype extraction, extraction of central tendency, and noise reduction
656(1)
B.2.6 Speed
656(1)
B.2.7 Local learning rule
657(5)
B.2.8 Implications of different types of coding for storage in pattern associators
662(1)
B.3 Autoassociation or attractor memory
663(23)
B.3.1 Architecture and operation
663(2)
B.3.2 Introduction to the analysis of the operation of autoassociation networks
665(2)
B.3.3 Properties
667(7)
B.3.4 Diluted connectivity and the storage capacity of attractor networks
674(11)
B.3.5 Use of autoassociation networks in the brain
685(1)
B.4 Competitive networks, including self-organizing maps
686(21)
B.4.1 Function
686(1)
B.4.2 Architecture and algorithm
687(1)
B.4.3 Properties
688(5)
B.4.4 Utility of competitive networks in information processing by the brain
693(1)
B.4.5 Guidance of competitive learning
694(2)
B.4.6 Topographic map formation
696(4)
B.4.7 Invariance learning by competitive networks
700(2)
B.4.8 Radial Basis Function networks
702(1)
B.4.9 Further details of the algorithms used in competitive networks
703(4)
B.5 Continuous attractor networks
707(11)
B.5.1 Introduction
707(2)
B.5.2 The generic model of a continuous attractor network
709(1)
B.5.3 Learning the synaptic strengths in a continuous attractor network
709(2)
B.5.4 The capacity of a continuous attractor network: multiple charts
711(1)
B.5.5 Continuous attractor models: path integration
712(3)
B.5.6 Stabilization of the activity packet within a continuous attractor network
715(2)
B.5.7 Continuous attractor networks in two or more dimensions
717(1)
B.5.8 Mixed continuous and discrete attractor networks
717(1)
B.6 Network dynamics: the integrate-and-fire approach
718(14)
B.6.1 From discrete to continuous time
718(2)
B.6.2 Continuous dynamics with discontinuities
720(3)
B.6.3 An integrate-and-fire implementation
723(2)
B.6.4 The speed of processing of attractor networks
725(2)
B.6.5 The speed of processing of a four-layer hierarchical network
727(4)
B.6.6 Spike response model
731(1)
B.7 Network dynamics: introduction to the mean-field approach
732(1)
B.8 Mean-field based neurodynamics
733(9)
B.8.1 Population activity
733(2)
B.8.2 The mean-field approach used in a model of decision-making
735(2)
B.8.3 The model parameters used in the mean-field analyses of decision-making
737(1)
B.8.4 A basic computational module based on biased competition
737(2)
B.8.5 Multimodular neurodynamical architectures
739(3)
B.9 Interacting attractor networks
742(3)
B.10 Sequence memory implemented by adaptation in an attractor network
745(1)
B.11 Error correction networks
745(8)
B.11.1 Architecture and general description
746(1)
B.11.2 Generic algorithm for a one-layer error correction network
746(1)
B.11.3 Capability and limitations of single-layer error-correcting networks
747(4)
B.11.4 Properties
751(2)
B.12 Error backpropagation multilayer networks
753(4)
B.12.1 Introduction
753(1)
B.12.2 Architecture and algorithm
753(3)
B.12.3 Properties of multilayer networks trained by error backpropagation
756(1)
B.13 Convolution networks
757(1)
B.14 Contrastive Hebbian learning: the Boltzmann machine
758(2)
B.15 Deep Belief Networks
760(1)
B.16 Reinforcement learning
760(7)
B.16.1 Associative reward-penalty algorithm of Barto and Sutton
761(2)
B.16.2 Reward prediction error or delta rule learning, and classical conditioning
763(1)
B.16.3 Temporal Difference (TD) learning
764(3)
B.17 Learning in the neocortex
767(2)
B.18 Forgetting in cortical associative neural networks, and memory reconsolidation
769(4)
B.19 Highlights
773(1)
C Information theory, and neuronal encoding
774(66)
C.1 Information theory
775(8)
C.1.1 The information conveyed by definite statements
775(1)
C.1.2 Information conveyed by probabilistic statements
776(1)
C.1.3 Information sources, information channels, and information measures
777(1)
C.1.4 The information carried by a neuronal response and its averages
778(3)
C.1.5 The information conveyed by continuous variables
781(2)
C.2 The information carried by neuronal responses
783(14)
C.2.1 The limited sampling problem
783(1)
C.2.2 Correction procedures for limited sampling
784(1)
C.2.3 The information from multiple cells: decoding procedures
785(4)
C.2.4 Information in the correlations between cells: a decoding approach
789(5)
C.2.5 Information in the correlations between cells: second derivative approach
794(3)
C.3 Information theory results
797(41)
C.3.1 The sparseness of the distributed encoding used by the brain
798(11)
C.3.2 The information from single neurons
809(3)
C.3.3 The information from single neurons: temporal codes versus rate codes
812(2)
C.3.4 The information from single neurons: the speed of information transfer
814(11)
C.3.5 The information from multiple cells: independence versus redundancy
825(4)
C.3.6 Should one neuron be as discriminative as the whole organism?
829(1)
C.3.7 The information from multiple cells: the effects of cross-correlations
830(4)
C.3.8 Conclusions on cortical neuronal encoding
834(4)
C.4 Information theory terms - a short glossary
838(1)
C.5 Highlights
839(1)
D Simulation software for neuronal networks, and information analysis of neuronal encoding
840(10)
D.1 Introduction
840(1)
D.2 Autoassociation or attractor networks
841(2)
D.2.1 Running the simulation
841(2)
D.2.2 Exercises
843(1)
D.3 Pattern association networks
843(3)
D.3.1 Running the simulation
843(2)
D.3.2 Exercises
845(1)
D.4 Competitive networks and Self-Organizing Maps
846(2)
D.4.1 Running the simulation
846(1)
D.4.2 Exercises
847(1)
D.5 Further developments
848(1)
D.6 Matlab code for a tutorial version of VisNet
848(1)
D.7 Matlab code for information analysis of neuronal encoding
849(1)
D.8 Matlab code to illustrate the use of spatial view cells in navigation
849(1)
D.9 Highlights
849(1)
References 850(74)
Index 924
Professor Edmund T. Rolls performs full-time research at the Oxford Centre for Computational Neuroscience, and at the University of Warwick, and has performed research and teaching for many years as Professor of Experimental Psychology at the University of Oxford, and as Fellow and Tutor of Corpus Christi College, Oxford. His research links computational neuroscience approaches to neurophysiological, human functional neuroimaging and neuropsychological studies in order to provide a fundamental basis for understanding human brain function and its disorders.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97801926444736e.html
Keywords: