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Noisy Brain: Stochastic Dynamics as a Principle of Brain Function [Hardback]

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(Pompeu Fabra University, Barcelona, Spain), (Oxford Centre for Computational Neuroscience, Oxford. UK)
  • Formāts: Hardback, 314 pages, height x width x depth: 253x177x20 mm, weight: 787 g, Illus.
  • Izdošanas datums: 28-Jan-2010
  • Izdevniecība: Oxford University Press
  • ISBN-10: 0199587868
  • ISBN-13: 9780199587865
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Noisy Brain: Stochastic Dynamics as a Principle of Brain FunctionPalielināt
  • Formāts: Hardback, 314 pages, height x width x depth: 253x177x20 mm, weight: 787 g, Illus.
  • Izdošanas datums: 28-Jan-2010
  • Izdevniecība: Oxford University Press
  • ISBN-10: 0199587868
  • ISBN-13: 9780199587865
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780199587865.html
The activity of neurons in the brain is noisy in that their firing times are random when they are firing at a given mean rate. This introduces a random or stochastic property into brain processing which we show in this book is fundamental to understanding many aspects of brain function, including probabilistic decision making, perception, memory recall, short-term memory, attention, and even creativity. In The Noisy Brain we show that in many of these processes, the noise caused by the random neuronal firing times is useful. However, this stochastic dynamics can be unstable or overstable, and we show that the stability of attractor networks in the brain in the face of noise may help to understand some important dysfunctions that occur in schizophrenia, normal aging, and obsessive-compulsive disorder.

The Noisy Brain provides a unifying computational approach to brain function that links synaptic and biophysical properties of neurons through the firing of single neurons to the properties of the noise in large connected networks of noisy neurons to the levels of functional neuroimaging and behaviour. The book describes integrate-and-fire neuronal attractor networks with noise, and complementary mean-field analyses using approaches from theoretical physics. The book shows how they can be used to understand neuronal, functional neuroimaging, and behavioural data on decision-making, perception, memory recall, short-term memory, attention, and brain dysfunctions that occur in schizophrenia, normal aging, and obsessive-compulsive disorder.

The Noisy Brain will be valuable for those in the fields of neuroscience, psychology, cognitive neuroscience, and biology from advanced undergraduate level upwards. It will also be of interest to those interested in neuroeconomics, animal behaviour, zoology, psychiatry, medicine, physics, and philosophy. The book has been written with modular chapters and sections, making it possible to select particular Chapters for course work. Advanced material on the physics of stochastic dynamics in the brain is contained in the Appendix.
1 Introduction: Neuronal, Cortical, and Network foundations 1
1.1 Introduction and overview
1
1.2 Neurons
3
1.3 Synaptic modification
4
1.4 Long-term potentiation and long-term depression
5
1.5 Neuronal biophysics
10
1.6 Action potential dynamics
11
1.7 Systems-level analysis of brain function
12
1.8 The fine structure of the cerebral neocortex
17
1.8.1 The fine structure and connectivity of the neocortex
17
1.8.2 Excitatory cells and connections
17
1.8.3 Inhibitory cells and connections
19
1.8.4 Quantitative aspects of cortical architecture
21
1.8.5 Functional pathways through the cortical layers
23
1.8.6 The scale of lateral excitatory and inhibitory effects, and the concept of modules
25
1.9 Backprojections in the cortex
26
1.9.1 Architecture
26
1.9.2 Recall
28
1.9.3 Attention
29
1.9.4 Backprojections, attractor networks, and constraint satisfaction
30
1.10 Autoassociation or attractor memory
30
1.10.1 Architecture and operation
32
1.10.2 Introduction to the analysis of the operation of autoassociation networks
33
1.10.3 Properties
35
1.10.4 Use of autoassociation networks in the brain
39
1.11 Noise, and the sparse distributed representations found in the brain
40
1.11.1 Definitions
41
1.11.2 Advantages of different types of coding
42
1.11.3 Firing rate distributions and sparseness
43
1.11.4 Information theoretic understanding of neuronal representations
57
2 Stochastic neurodynamics 65
2.1 Introduction
65
2.2 Network dynamics: the integrate-and-fire approach
65
2.2.1 From discrete to continuous time
66
2.2.2 Continuous dynamics with discontinuities: integrate-and-fire neuronal networks
67
2.2.3 An integrate-and-fire implementation with NMDA receptors and dynamical synapses
71
2.3 Attractor networks, energy landscapes, and stochastic dynamics
73
2.4 Reasons why the brain is inherently noisy and stochastic
78
2.5 Brain dynamics with and without stochasticity: an introduction to mean-field theory
80
2.6 Network dynamics: the mean-field approach
81
2.7 Mean-field based theory
82
2.7.1 Population activity
83
2.7.2 The mean-field approach used in the model of decision-making
85
2.7.3 The model parameters used in the mean-field analyses of decision-making
87
2.7.4 Mean-field neurodynamics used to analyze competition and cooperation between networks
88
2.7.5 A consistent mean-field and integrate-and-fire approach
88
3 Short-term memory and stochastic dynamics 91
3.1 Introduction
91
3.2 Cortical short-term memory systems and attractor networks
91
3.3 Prefrontal cortex short-term memory networks, and their relation to perceptual networks
94
3.4 Computational necessity for a separate, prefrontal cortex, short-term memory system
98
3.5 Synaptic modification is needed to set up but not to reuse short-term memory systems
98
3.6 What, where, and object–place combination short-term memory in the prefrontal cortex
99
3.7 Hierarchically organized series of attractor networks
100
3.8 Stochastic dynamics and the stability of short-term memory
102
3.8.1 Analysis of the stability of short-term memory
103
3.8.2 Stability and noise in the model of short-term memory
104
3.8.3 Alterations of stability
106
3.9 Memory for the order of items in short-term memory
114
3.10 Stochastic dynamics and long-term memory
120
4 Attention and stochastic dynamics 121
4.1 Introduction
121
4.2 Biased competition—single neuron studies
121
4.3 A basic computational module for biased competition
126
4.4 The neuronal and biophysical mechanisms of attention
128
4.5 Stochastic dynamics and attention
132
4.6 Disengagement of attention, and neglect
135
4.7 Decreased stability of attention produced by alterations in synaptically activated ion channels
135
4.8 Increased stability of attention produced by alterations in synaptically activated ion channels
137
5 Probabilistic decision-making 139
5.1 Introduction
139
5.2 Decision-making in an attractor network
140
5.3 The neuronal data underlying a vibrotactile discrimination task
141
5.4 Theoretical framework: a probabilistic attractor network
144
5.5 Stationary multistability analysis: mean-field
146
5.6 Non-stationary probabilistic analysis: spiking dynamics
149
5.6.1 Integrate-and-fire simulations of decision-making
149
5.6.2 Decision-making on a single trial
149
5.6.3 The probabilistic nature of the decision-making
151
5.6.4 Probabilistic decision-making and Webers law
153
5.6.5 Reaction times
156
5.6.6 Finite-size noise effects
157
5.7 Properties of this model of decision-making
159
5.7.1 Comparison with other models of decision-making
159
5.7.2 Integration of evidence by the attractor network, escaping time, and reaction times
160
5.7.3 Distributed decision-making
161
5.7.4 Weber's law
162
5.7.5 Unifying principles
163
5.8 A multistable system with noise
164
6 Confidence and decision-making 167
6.1 The model of decision-making
168
6.2 Neuronal responses on difficult vs easy trials, and decision confidence
171
6.3 Reaction times of the neuronal responses
174
6.4 Percentage correct
175
6.5 Simulation of fMRI signals: haemodynamic convolution of synaptic activity
175
6.6 Prediction of the BOLD signals on difficult vs easy decision-making trials
177
6.7 Neuroimaging investigations of task difficulty, and confidence
180
6.7.1 Olfactory pleasantness decision task
180
6.7.2 Temperature pleasantness decision task
181
6.7.3 fMRI analyses
182
6.7.4 Brain areas with activations related to easiness and confidence
182
6.8 Correct decisions vs errors, and confidence
185
6.8.1 Operation of the attractor network model on correct vs error trials
185
6.8.2 Predictions of fMRI BOLD signals from the model
189
6.8.3 fMRI BOLD signals that are larger on correct than error trials
190
6.8.4 fMRI signals linearly related to choice easiness with correct vs incorrect choices
191
6.8.5 Evaluation of the model: a basis for understanding brain processes and confidence for correct vs incorrect decisions
193
6.9 Decisions based on confidence in one's decisions: self-monitoring
196
6.9.1 Decisions about confidence estimates
196
6.9.2 A theory for decisions about confidence estimates
196
6.9.3 Decisions about confidence estimates: neurophysiological evidence
203
6.9.4 Decisions about decisions: self-monitoring
206
6.10 Synthesis: decision confidence, noise, neuronal activity, the BOLD signal, and self-monitoring
207
6.10.1 Why there are larger BOLD signals for easy vs difficult decisions
207
6.10.2 Validation of BOLD signal magnitude related to the easiness of a decision as a signature of neural decision-making
207
6.10.3 Predictions of neuronal activity during decision-making
208
6.10.4 Multiple types of decision are made, each in its own brain region
208
6.10.5 The encoding of decision confidence in the brain
209
6.10.6 Self-monitoring: correction of previous decisions
211
7 Perceptual detection and stochastic dynamics 213
7.1 Introduction
213
7.2 Psychophysics and neurophysiology of perceptual detection
213
7.3 Computational models of probabilistic signal detection
215
7.4 Stochastic resonance
217
7.5 Synthesis
218
8 Applications of this stochastic dynamical theory to brain function 219
8.1 Introduction
219
8.2 Memory recall
219
8.3 Decision-making with multiple alternatives
219
8.4 Perceptual decision-making and rivalry
220
8.5 The matching law
221
8.6 Symmetry-breaking
222
8.7 The evolutionary utility of probabilistic choice
222
8.8 Selection between conscious vs unconscious decision-making, and free will
223
8.9 Creative thought
224
8.10 Unpredictable behaviour
224
8.11 Dreams
225
8.12 Multiple decision-making systems in the brain
226
8.13 Stochastic noise, attractor dynamics, and aging
226
8.13.1 NMDA receptor hypofunction
227
8.13.2 Dopamine
229
8.13.3 Impaired synaptic modification
230
8.13.4 Cholinergic function
230
8.14 Stochastic noise, attractor dynamics, and schizophrenia
235
8.14.1 Introduction
235
8.14.2 A dynamical systems hypothesis of the symptoms of schizophrenia
236
8.14.3 The depth of the basins of attraction: mean-field flow analysis
237
8.14.4 Decreased stability produced by reductions of NMDA receptor activated synaptic conductances
238
8.14.5 Increased distractibility produced by reductions of NMDA receptor activated synaptic conductances
239
8.14.6 Signal-to-noise ratio in schizophrenia
239
8.14.7 Synthesis: network instability and schizophrenia
240
8.15 Stochastic noise, attractor dynamics, and obsessive-compulsive disorder
244
8.15.1 Introduction
244
8.15.2 A hypothesis about obsessive-compulsive disorder
245
8.15.3 Glutamate and increased depth of the basins of attraction of attractor networks
247
8.15.4 Synthesis on obsessive-compulsive disorder
249
8.16 Predicting a decision before the evidence is applied
251
8.17 Decision-making between interacting individuals
253
8.18 Unifying principles of cortical design
253
8.19 Apostasis
257
A Mean-field analyses, and stochastic dynamics 261
A.1 The Integrate-and-Fire model
261
A.2 The population density approach
262
A.3 The diffusion approximation
263
A.4 The mean-field model
265
A.5 Introducing noise into a mean-field theory
267
A.6 Effective reduced rate-models of spiking networks: a data-driven Fokker–Planck approach
268
A.6.1 A reduced rate-model of spiking networks
268
A.6.2 One-dimensional rate model
271
References 277
Index 300
B Colour Plates 303
Gustavo Deco (born in 1961 in Argentina) is Research Professor from the Institució Catalana de Recerca i Estudis Avanēats at the Pompeu Fabra University (UPF) where he is leading the Computational Neuroscience group at the Department of Technology and he is also director of the doctoral program in Computer Science and Digital Communication. His research interest includes computational neuroscience, neuropsycholgy, psycholinguistics, biological networks, statistical formulation of neural networks, and chaos theory.

Edmund T. Rolls is a neuroscientist at The Oxford Centre for Computational Neuroscience, Oxford, and was Professor of Experimental Psychology at the University of Oxford, and a Fellow and Tutor of Corpus Christi College, Oxford. He performs research linking 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.