Ebook: Lectures in Supercomputational Neurosciences: Dynamics in Complex Brain Networks

Computational Neuroscience is a burgeoning field of research where only the combined effort of neuroscientists, biologists, psychologists, physicists, mathematicians, computer scientists, engineers and other specialists, e.g. from linguistics and medicine, seem to be able to expand the limits of our knowledge.

The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research. Worked-out examples of applications are presented for networks of Morris-Lecar neurons to model the cortical connections of a cat's brain, supported with data from experimental studies.

This book is particularly suited for graduate students and nonspecialists from related fields with a general science background, looking for a substantial but "hands-on" introduction to the subject matter.

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Computational Neuroscience is a burgeoning field of research where only the combined effort of neuroscientists, biologists, psychologists, physicists, mathematicians, computer scientists, engineers and other specialists, e.g. from linguistics and medicine, seem to be able to expand the limits of our knowledge.

The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research. Worked-out examples of applications are presented for networks of Morris-Lecar neurons to model the cortical connections of a cat's brain, supported with data from experimental studies.

This book is particularly suited for graduate students and nonspecialists from related fields with a general science background, looking for a substantial but "hands-on" introduction to the subject matter.

---

Computational Neuroscience is a burgeoning field of research where only the combined effort of neuroscientists, biologists, psychologists, physicists, mathematicians, computer scientists, engineers and other specialists, e.g. from linguistics and medicine, seem to be able to expand the limits of our knowledge.

The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research. Worked-out examples of applications are presented for networks of Morris-Lecar neurons to model the cortical connections of a cat's brain, supported with data from experimental studies.

This book is particularly suited for graduate students and nonspecialists from related fields with a general science background, looking for a substantial but "hands-on" introduction to the subject matter.

Content:
Front Matter....Pages I-X
Front Matter....Pages 1-1
Foundations of Neurophysics....Pages 3-48
Synapses and Neurons: Basic Properties and Their Use in Recognizing Environmental Signals....Pages 49-74
Front Matter....Pages 75-75
Structural Characterization of Networks Using the Cat Cortex as an Example....Pages 77-106
Organization and Function of Complex Cortical Networks....Pages 107-133
Synchronization Dynamics in Complex Networks....Pages 135-175
Synchronization Analysis of Neuronal Networks by Means of Recurrence Plots....Pages 177-191
Front Matter....Pages 193-193
Neural and Cognitive Modeling with Networks of Leaky Integrator Units....Pages 195-223
A Dynamic Model of the Macrocolumn....Pages 225-247
Front Matter....Pages 249-249
Building a Large-Scale Computational Model of a Cortical Neuronal Network....Pages 251-266
Maintaining Causality in Discrete Time Neuronal Network Simulations....Pages 267-278
Sequential and Parallel Implementation of Networks....Pages 279-315
Front Matter....Pages 317-317
Parametric Studies on Networks of Morris-Lecar Neurons....Pages 319-330
Traversing Scales: Large Scale Simulation of the Cat Cortex Using Single Neuron Models....Pages 331-342
Parallel Computation of Large Neuronal Networks with Structured Connectivity....Pages 343-367
Back Matter....Pages 369-375

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Computational Neuroscience is a burgeoning field of research where only the combined effort of neuroscientists, biologists, psychologists, physicists, mathematicians, computer scientists, engineers and other specialists, e.g. from linguistics and medicine, seem to be able to expand the limits of our knowledge.

The present volume is an introduction, largely from the physicists' perspective, to the subject matter with in-depth contributions by system neuroscientists. A conceptual model for complex networks of neurons is introduced that incorporates many important features of the real brain, such as various types of neurons, various brain areas, inhibitory and excitatory coupling and the plasticity of the network. The computational implementation on supercomputers, which is introduced and discussed in detail in this book, will enable the readers to modify and adapt the algortihm for their own research. Worked-out examples of applications are presented for networks of Morris-Lecar neurons to model the cortical connections of a cat's brain, supported with data from experimental studies.

This book is particularly suited for graduate students and nonspecialists from related fields with a general science background, looking for a substantial but "hands-on" introduction to the subject matter.

Content:
Front Matter....Pages I-X
Front Matter....Pages 1-1
Foundations of Neurophysics....Pages 3-48
Synapses and Neurons: Basic Properties and Their Use in Recognizing Environmental Signals....Pages 49-74
Front Matter....Pages 75-75
Structural Characterization of Networks Using the Cat Cortex as an Example....Pages 77-106
Organization and Function of Complex Cortical Networks....Pages 107-133
Synchronization Dynamics in Complex Networks....Pages 135-175
Synchronization Analysis of Neuronal Networks by Means of Recurrence Plots....Pages 177-191
Front Matter....Pages 193-193
Neural and Cognitive Modeling with Networks of Leaky Integrator Units....Pages 195-223
A Dynamic Model of the Macrocolumn....Pages 225-247
Front Matter....Pages 249-249
Building a Large-Scale Computational Model of a Cortical Neuronal Network....Pages 251-266
Maintaining Causality in Discrete Time Neuronal Network Simulations....Pages 267-278
Sequential and Parallel Implementation of Networks....Pages 279-315
Front Matter....Pages 317-317
Parametric Studies on Networks of Morris-Lecar Neurons....Pages 319-330
Traversing Scales: Large Scale Simulation of the Cat Cortex Using Single Neuron Models....Pages 331-342
Parallel Computation of Large Neuronal Networks with Structured Connectivity....Pages 343-367
Back Matter....Pages 369-375
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