Front Cover

The Rewiring Brain

Copyright Page

Contents

List of Contributors

Editorial

1 Introduction

2 Experimental Background

3 Homeostatic Structural Plasticity

4 Structural Plasticity and Connectivity

5 Structural Plasticity and Learning and Memory

6 Neurogenesis-Related Structural Plasticity

7 Structural Plasticity and Pathology

8 Outlook

References

I. Experimental Background

1 Structural Plasticity and Cortical Connectivity

1 Introduction

2 The Role of Structural Synaptic Plasticity in Hebb’s Theory of Cell Assemblies

3 Structural Plasticity Following Enriched Experience

4 Structural Plasticity Following Sensory Deprivation or Stimulation

5 Structural Plasticity in Learning and Memory

6 Structural Plasticity and Long-Term Functional Synaptic Plasticity

7 Activity-Dependent and -Independent Structural Synaptic Plasticity

8 Structural Plasticity and Cortical Connectivity

8.1 Large-Scale Structural Plasticity

8.2 Microscopic Structural Plasticity and Cortical Connectivity

8.3 Mechanisms of Microscopic Structural Plasticity Influencing Cortical Connectivity

9 Future Perspectives

Acknowledgments

References

Further Reading

2 Structural Plasticity Induced by Adult Neurogenesis

1 Introduction

2 Structural Rewiring Induced by Adult Neurogenesis: Anatomical and Morphological Evidence

3 Newborn Neurons Promote Their Own Integration: Electrophysiological Evidence

4 Local Microenvironments Support Ongoing Neuronal Integration

5 Synaptic Rewiring by New Neurons: Balancing the Firing Budget

6 Conclusion

Acknowledgment

References

3 Structural Neural Plasticity During Stroke Recovery

1 Introduction

2 Animal Models of Stroke

3 Axonal Sprouting and Rewiring Connections

4 Dendritic Arbor Remodeling

5 Dendritic Spine Plasticity

6 Perspectives and Future Directions

6.1 Technological Developments

6.2 Minimizing the Dark Side of Structural Plasticity Through Intelligent Intervention

6.3 Computational Modeling Studies

References

4 Is Lesion-Induced Synaptic Rewiring Driven by Activity Homeostasis?

1 Introduction

2 Current View and Limitations

2.1 Current View

2.2 Limitations

3 Homeostatic Structural Plasticity

3.1 Hypothesis

3.2 Expectations

4 In Vitro Indications for Homeostatic Structural Plasticity

4.1 Dendritic Spines

4.2 Dendrite and Axon Outgrowth

4.3 Minimum Activity for Spine Formation and Neurite Outgrowth

5 In Vivo Indications for Homeostatic Structural Plasticity

5.1 Visual Cortex

5.2 Barrel Cortex

5.3 Stroke

6 Experimental Testing of Homeostatic Structural Plasticity

6.1 Growth Curves

6.2 Activity Restoration

7 Discussion

7.1 Relation to Other Forms of Plasticity

7.2 Cortical Remapping

7.3 Maladaptive Responses

7.4 Neurodegeneration

7.5 Neurological Therapy

7.6 Computational Modeling

8 Conclusion

References

II. Homeostatic Structural Plasticity

5 Network Formation Through Activity-Dependent Neurite Outgrowth: A Review of a Simple Model of Homeostatic Structural Plas...

1 Introduction

2 Model

2.1 Overview

2.2 Neuron Model

2.3 Outgrowth and Connectivity

2.4 Parameters

3 Results

3.1 Network Assembly, Overshoot, and Homeostasis

3.2 Relationship Between Activity and Connectivity

3.3 Slow Fluctuations in Activity

3.4 Effect of Inhibition on Overshoot

3.5 Multiple Equilibrium States

3.6 Differentiation Between Excitatory and Inhibitory Cells

3.7 Patchy Connectivity Structure

3.8 Self-Repair of Connectivity After Lesions

3.9 Neurogenesis-Induced Network Reorganization

3.10 Neuronal Death During Development

3.11 Differentiation of Intrinsic Properties

3.12 Self-Organized Criticality

3.13 Retinal Mosaics

3.14 Developmental Changes in Burst Patterns

3.15 Developmental Transitions in Cognition

4 Discussion

4.1 Future Experimental Studies

4.2 Future Modeling Studies

References

6 Clustered Arrangement of Inhibitory Neurons Can Lead to Oscillatory Dynamics in a Model of Activity-Dependent Structural ...

1 Introduction

2 Model

3 Model Implementation

4 Methods

4.1 Parameter Choice

4.2 Spatial Arrangement of Excitatory and Inhibitory Neurons

4.3 Measuring Spatial Clustering of Inhibition in 1D and 2D Neuron Arrangements

4.3.1 1D Inhibitory Clustering Measure

4.3.2 2D Inhibitory Clustering Measure

5 Results

5.1 1D Results

5.1.1 The Proportion of Inhibitory Neurons—A Necessary Condition for Global System Behavior

5.1.2 The Effect of Inhibitory Clustering on Global System Behavior in 1D

5.1.3 Is There an Optimal Proportion of Inhibition for Inducing Oscillatory Behavior in 1D Networks Containing Inhibitory C...

5.1.4 Network Analysis of 1D Global Behavior Types

Total Stabilization

Stable Oscillations

Unstable Oscillations

Unbounded Growth

5.2 2D Results

5.2.1 The Effect of Inhibitory Clustering on Global System Behavior in 2D

6 Discussion

6.1 Comparison to Previous Modeling Results

6.2 1D Network Behaviors

6.3 2D Network Behaviors

6.4 Future Modeling Studies

6.5 Future Experimental Studies

6.6 Concluding Remarks

Acknowledgment

References

7 A Detailed Model of Homeostatic Structural Plasticity Based on Dendritic Spine and Axonal Bouton Dynamics

1 Introduction

2 Model

2.1 The Model at a Glance

2.2 Electrical Activity

2.3 Growth Rules

2.3.1 Definitions and Time Scales

2.3.2 Measures of Electrical Activity

2.3.3 General Requirements for Growth Rules

2.3.4 Finding Appropriate Growth Rules

2.4 Synapse Formation and Network Topology

2.5 Synapse Deletion

3 Model Results

3.1 Comparing MSP Results With Experimental Data

4 Discussion

4.1 Future Experimental Studies

4.2 Future Modeling Studies

References

8 Critical Periods Emerge from Homeostatic Structural Plasticity in a Full-Scale Model of the Developing Cortical Column

1 Introduction

2 MSP in a Nutshell

3 MSP Implementation in NEST

4 Critical Periods in a Self-Organizing Two-Population Network

5 Inhibition Triggers the Onset of Critical Periods

6 Growing a Virtual Cortical Column from Scratch

7 Low Target Activity Levels Impose Pronounced Synaptic Rewiring

8 Comparison Between Self-Organizing and Reconstructed Connectivity

9 Scalability Limitation of MSP

9.1 Update of Electrical Activity

9.2 Update of Synaptic Elements

9.3 Update of Connectivity

10 A Scalable Algorithm for MSP

10.1 Tree Construction

10.2 Tree Update

10.3 Target Neuron Selection

10.4 Error Analysis

10.5 Summary

11 Results of the Scalable Algorithm

11.1 Performance

11.2 Accuracy

12 Discussion

12.1 Future Experimental Studies

12.2 Future Modeling Studies

13 Conclusion

Acknowledgments

References

9 Lesion-Induced Dendritic Remodeling as a New Mechanism of Homeostatic Structural Plasticity in the Adult Brain

1 Introduction

2 Model

3 Results

4 Discussion

5 Future Experimental Studies

6 Future Modeling Studies

7 A General Principle for Homeostatic Dendritic Plasticity

8 Potential Synergy With Homeostatic Structural Plasticity of the Axon Initial Segment

9 Clinical Relevance

Acknowledgments

References

III. Structural Plasticity and Connectivity

10 The Role of Structural Plasticity in Producing Nonrandom Neural Connectivity

1 Introduction

1.1 Structural Plasticity

1.2 Changing Connections in a Stable Manner

1.3 Accounting for Nonrandom Features of Neural Circuits

2 Details of Our Model

2.1 Heterogeneous Inputs to the Circuit

2.2 Types of Functional Plasticity

2.3 Simulating Structural Plasticity

2.4 Measuring the Excess Abundance of Motifs

2.5 Training Protocol

3 Behavior of the Trained Network

4 Structural Plasticity Produces Highly Interconnected Assemblies of Functionally Similar Cells

5 Differences in Network Topography Across Structural Plasticity Mechanisms

6 Changes in the Connectivity Pattern Impact the Distribution of Connection Strengths

7 Discussion

7.1 Future Experimental Studies

7.2 Future Modeling Studies

Summary

References

11 Structural Plasticity and the Generation of Bidirectional Connectivity

1 Introduction

2 Self-Organization of Recurrent Cortical Wiring

3 Topology, the Jensen Inequality, and Bidirectional Connections

4 A Markov Model of Competing Connectivity Biases

5 Bidirectional Connections in the Presence of Inhibitory STDP

6 Discussion

6.1 Future Experimental Studies

6.2 Future Modeling Studies

References

12 Spike Timing-Dependent Structural Plasticity of Multicontact Synaptic Connections

1 Introduction

2 Local Correlation Detection

3 Connections Made of Multiple Contacts

4 STDP Model of Spine Plasticity and Turnover

5 Discussion

5.1 Functional Significance of Multiple Synaptic Contacts

5.2 Relations to Previous Models

5.3 Future Experimental Studies

5.4 Future Modeling Studies

Acknowledgments

References

13 Selection of Synaptic Connections by Wiring Plasticity for Robust Learning by Synaptic Weight Plasticity

1 Introduction

2 Model

2.1 Neural Model of an Inference Task

2.2 Coding Strategies for Sparsely Connected Networks

2.3 Optimality of Connectivity

2.4 Synaptic Weight Plasticity and Wiring Plasticity

2.5 Details of Simulations

3 Results

3.1 Coding by Synaptic Connections Enables Signal Variability Reduction

3.2 Dual Coding by Synaptic Weights and Connections Enables Robust Inference

3.3 Hebbian Wiring Plasticity Prevents Information Loss by Spine Elimination

3.4 Variance Learning With Synaptic Wiring Plasticity

4 Discussion

4.1 Future Experimental Studies

4.2 Future Theoretical Studies

Acknowledgment

References

IV. Structural Plasticity and Learning and Memory

14 Within a Spine’s Reach

1 Introduction

2 Microstructural Plasticity: Spine Dynamics and the Making and Breaking of Synaptic Connections

3 What Is Within a Spine’s Reach?

4 Distributed Versus Clustered Inputs

5 Experimental Tests of the Predictions of the Input Clustering Hypothesis

5.1 Do Dendritic Input Clusters Exist?

5.2 Does Experience-Dependent Remodeling Drive Formation of Dendritic Input Clusters?

6 Cellular and Molecular Mechanisms Driving Input Cluster Formation

7 Future Experimental Studies

7.1 What Is the Functional Significance of Clustering?

7.2 How Do Clusters Form?

7.3 Does Clustering Occur at Higher Levels in the Network?

7.4 Did Clustering Evolve in Circuits With Higher Demands for Adaptive Pattern Recognition?

8 Future Modeling Studies

8.1 Neurorealistic Simulation

8.2 Neuromorphic Computing

References

15 Impact of Structural Plasticity on Memory Capacity

1 Introduction

1.1 Chapter Organization and Content

2 Forms of Structural Plasticity

2.1 Synaptogenesis

2.2 Filopodia Dynamics

2.3 Ca2+ Influx as a Key Determinant of Synaptic Plasticity and Structural Rearrangements

2.4 Morphological Changes of the Dendritic Spines

2.5 Learning-Induced Synapse Clustering

2.6 Dendritic and Axonal Remodeling

2.7 Structural Plasticity and Dendritic Nonlinearities

3 Computational Models of Structural Plasticity and Memory Formation

3.1 Toward Hardware Implementations of Structural Plasticity Models

4 Discussion

4.1 Future Experimental Studies

4.2 Future Modeling Studies

Acknowledgment

References

16 Long-Term Information Storage by the Interaction of Synaptic and Structural Plasticity

1 Introduction

2 Model

3 Connectivity Emerging from the Interaction of Synaptic and Structural Plasticity

4 Necessary Conditions to Yield a Bimodal Distribution of the Number of Synapses

5 Stimulation-Dependent Changes of the Stationary Connectivity

6 Generalization of the Model for Investigating Dynamics

7 Information Retention at the Biological Working Point

8 Information Retention at Altered Stimulation Levels

9 Information Can Be Stored Faster Than It Decays

10 Discussion

10.1 Future Modeling Studies

10.2 Future Experimental Studies

References

17 Impact of Structural Plasticity on Memory Formation and Decline

1 Introduction

2 Modeling Framework

2.1 Memories and Cell Assemblies

2.2 Memory Retrieval, Storage Capacity, and Output Noise

2.3 Synaptic Weight Plasticity

2.4 Structural Plasticity

2.5 Effectual Connectivity

3 Results

3.1 Structural Plasticity, Learning Load, and Distribution of Synaptic Weights and Numbers

3.2 Impact of Increasing Peff on Retrieval Quality and Storage Efficiency

3.3 Impact of Increasing Peff on the Memory Capacity of a Cortical Macrocolumn

3.4 Structural Synaptic Plasticity and the Cellular Basis of the Spacing Effect

3.5 Gradients in Peff and Graded Amnesia

3.6 Memory Decline, CF, and the Stability–Plasticity Dilemma

4 Discussion

4.1 Future Experimental Studies

4.2 Future Modeling Studies

References

V. Neurogenesis-Related Structural Plasticity

18 Adult Neurogenesis and Synaptic Rewiring in the Hippocampal Dentate Gyrus

1 Introduction

2 Experimental Study

2.1 Design of Study

2.2 Measuring Synaptic Rewiring

3 Results of Experimental Study

4 Computational Model

5 Results of Computational Model

5.1 Number of Free Excitatory and Inhibitory Axonal Elements

5.2 Number of Remodeling Excitatory and Inhibitory Axonal Elements

5.3 Inverse Relationship Between CP Rate and Number of Free Axonal Elements

5.4 Number of Free Dendritic Elements

6 Discussion

6.1 Experimental Study

6.2 Modeling Study

6.3 Future Experimental Studies

6.4 Future Modeling Studies

References

19 Modifications in Network Structure and Excitability May Drive Differential Activity-Dependent Integration of Granule Cel...

1 Introduction

2 Structure and Integration Sequence into Healthy DG Networks

3 Pathological Integration of Newly Born Neurons and Its Functional Correlates

4 Understanding Network Driven Effects of Neuronal Incorporation During Adult Neurogenesis

5 Cellular Correlates of Integration Patterns of New Cells into DG Circuits

6 Discussion

6.1 Future Experimental Studies

6.2 Future Modeling Studies

Acknowledgments

References

20 Computational Perspectives on Adult Neurogenesis

1 Introduction

2 The Addition of New Neurons Into ANNs

3 Biological Context of Neurogenesis

3.1 The Hippocampus

3.2 The Dentate Gyrus

3.3 The Maturation of New Granule Cells

4 Anatomically Constrained Computational Models of Neurogenesis

5 Challenges Facing Neurogenesis Model Simulation and Description

6 N2A: A Neural Modeling Framework With Support for Structural Plasticity

7 Discussion

7.1 Future Experimental Studies

7.2 Future Modeling Studies

Acknowledgments

References

21 Restricted Boltzmann Machine Models of Hippocampal Coding and Neurogenesis

1 Introduction

2 A RBM Model of Learning and Neurogenesis in the DG

3 The Role of Young DGCs in Memory Encoding

4 Simulating the Emergence of Place Cells in RBMs With More Naturalist Inputs

5 The Effect of Young DGCs on Learning in the Full Hippocampal Model

6 Discussion

6.1 Future Experimental Studies

6.2 Future Modeling Studies

References

VI. Structural Plasticity and Pathology

22 Modeling the Impact of Lesions in the Brain

1 Introduction

2 Models of Networks and Their Dynamics

2.1 Network Centrality

2.2 Hierarchical Modular Network

2.3 Dynamic Effects of Virtual Lesions

2.4 Plasticity and Recovery From Virtual Lesions

2.5 Further Applications of Lesion Modeling

3 Results

3.1 Important Nodes and Edges: Theoretical Studies

3.2 Important Nodes and Edges: Experimental Studies

4 Discussion

4.1 Important Nodes and Edges: Brain Diseases

4.2 Future Experimental Studies

4.3 Future Modeling Studies

Glossary of Network Analysis Terms

References

23 Network Models of Epilepsy-Related Pathological Structural and Functional Alterations in the Dentate Gyrus

1 Introduction

2 Model

2.1 DG Cell Types, Numbers, and Connections

2.2 Structural Alterations in the Dentate During Epileptogenesis

3 Results

3.1 The Role of Mossy Fiber Sprouting and Interneuron Loss in Determining Network Excitability

3.2 Network Architectures That Induce Hyperexcitability

4 Discussion

4.1 Future Modeling Studies

4.2 Future Experimental Studies

Acknowledgments

References

24 Computational Models of Stroke Recovery

1 Introduction

2 Models of Neuromotor Recovery

2.1 Models of Recovery at Central Level: Focal Cortical Lesions and Activity-Dependent Reorganization

2.2 Models at Functional Level

2.2.1 Temporal Evolution of Performance Over Training Time

2.2.2 The Role of Physical Assistance

2.3 Multilevel Models

3 Toward a Computational Rehabilitation

3.1 Do Computational Models Provide Testable Hypotheses on the Mechanisms of Recovery?

3.2 Can Computational Models Predict the Recovery on a Single Patient Basis?

3.3 Do Models Allow Designing Patient-Specific “Optimal” Therapy?

4 Directions for Future Research

4.1 Future Modeling Studies

4.2 Future Experimental Studies

5 Conclusions

Acknowledgments

References

25 Neural Plasticity in Human Brain Connectivity: The Effects of Deep Brain Stimulation

1 Introduction

2 Deep Brain Stimulation

3 Deep Brain Stimulation–Induced Functional Connectivity Changes

4 Deep Brain Stimulation–Induced Structural Connectivity Changes

5 Impact of Structural Connectivity Changes on Brain Dynamics

6 Discussion

6.1 Future Experimental Studies

6.2 Future Modeling Studies

References

Index

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