The cortex and the critical point understanding the power of emergence
"A survey of the criticality hypothesis which imports theory from physics to understand the brain and could be a grand unifying theory of the brain at a time when neuroscience is dominated by data"--
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Cambridge, Massachusetts :
The MIT Press
[2022]
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| Edición: | 1st ed |
| Colección: | The MIT Press
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009689915406719 |
Tabla de Contenidos:
- Intro
- Contents
- Acknowledgments
- Introduction
- The Critical Point in Context
- The Goals and Structure of This Book
- I. Background
- 1. The Main Idea
- A Simple Model
- Optimal Information Processing
- The Appearance of Emergent Phenomena
- Power Laws
- Avalanches
- A Phase Transition
- From a Model to Data
- The Criticality Hypothesis
- Objections and Responses to the Criticality Hypothesis
- Chapter Summary
- 2. Emergent Phenomena
- Methodological Reductionism
- The Wave as an Emergent Phenomenon
- Emergent Phenomena in the Brain
- A Simple Model of Emergent Phenomena in the Brain
- Complex Emergent Phenomena Occur at a Phase Transition
- More Complex Emergent Phenomena?
- How to Study Emergent Phenomena
- Chapter Summary
- II. The Critical Point and Its Consequences
- 3. The Critical Point
- The Branching Model: A Branching Ratio Near 1
- The Branching Model: A Phase Transition with Control and Order Parameters
- The Branching Model: An Exponent Relation between Multiple Power Laws
- The Branching Model: Fractal Copies of Avalanches
- Signatures of Being near the Critical Point
- Signatures of the Critical Point from the Data
- In Vitro Experiments
- Data: A Branching Ratio near 1
- Data: A Phase Transition with Control and Order Parameters
- Data: An Exponent Relation between Multiple Power Laws
- Data: Fractal Copies of Avalanches
- Objections to These Signatures of Criticality
- Chapter Summary
- 4. Optimality
- The Branching Model: Information Transmission
- The Branching Model: Dynamic Range
- The Branching Model: Susceptibility
- Data: Dynamic Range
- Data: Information Transmission
- Data: Susceptibility
- Other Predictions Yet to Be Tested
- Chapter Summary
- 5. Universality
- Universality in Physical Systems
- Universality in the Cortex: Indicators.
- Indicators Seen across Species
- Indicators Seen across Scales
- Described by a Simple Model
- Chapter Summary
- III. Future Directions
- 6. Homeostasis and Health
- Homeostasis toward the Critical Point after a Major Perturbation
- Sleep and Homeostasis toward the Critical Point
- Sensory Adaptation toward the Critical Point
- Development toward the Critical Point
- Themes from Homeostasis Results
- Health
- Chapter Summary
- 7. Quasicriticality
- Universality: Unfinished Issues
- A Possible Solution: Quasicriticality
- Another View: Slightly Subcritical
- Another View: Subsampling
- Another View: Griffiths Phase
- Chapter Summary
- 8. Cortex
- The Expansion of Cortical Area
- Associations of Associations
- The Special Role of Layers 2 and 3
- Multifunctionality and the Critical Point
- Nearly Critical in Layers 2 and 3, but Not in Layer 5
- Staying Nearly Critical While Learning
- Timescales throughout the Hierarchy
- Chapter Summary
- 9. Epilogue
- What We Know
- What We Don't Know
- Frontier Issues
- What I Did Not Cover
- Appendix
- Relation between Power-Law Exponent and Slope (Chapters 1 and 6)
- When the Average Value of a Power Law Diverges and When It Does Not (Chapters 1 and 6)
- Long-Range Temporal Correlations (Chapters 1, 6, and 8)
- Informal Derivation of the Exponent Relation (Chapters 3, 5, 6, 7, and 8)
- Avalanche Shape Collapse (Chapters 3, 5, 6, and 8)
- How to Quantify Network Dynamics (Chapters 4 and 8)
- Software and Data for Exercises and Analyses
- Notes
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- References
- Index.
