The Functional Role of Critical Dynamics in Neural Systems  [electronic resource] /
Tomen, Nergis.editor.
Herrmann, J. Michael.editor.
Ernst, Udo.editor.
This book offers a timely overview of theories and methods developed by an authoritative group of researchers to understand the link between criticality and brain functioning. Cortical information processing in particular and brain function in general rely heavily on the collective dynamics of neurons and networks distributed over many brain areas. A key concept for characterizing and understanding brain dynamics is the idea that networks operate near a critical state, which offers several potential benefits for computation and information processing. However, there is still a large gap between research on criticality and understanding brain function. For example, cortical networks are not homogeneous but highly structured, they are not in a state of spontaneous activation but strongly driven by changing external stimuli, and they process information with respect to behavioral goals. So far the questions relating to how critical dynamics may support computation in this complex setting, and whether they can outperform other information processing schemes remain open. Based on the workshop “Dynamical Network States, Criticality and Cortical Function", held in March 2017 at the Hanse Institute for Advanced Studies (HWK) in Delmenhorst, Germany, the book provides readers with extensive information on these topics, as well as tools and ideas to answer the above-mentioned questions. It is meant for physicists, computational and systems neuroscientists, and biologists.
Avalanche dynamics and correlations in neural systems -- Playing at the edge of criticality: Expanded whole-brain repertoire of connectome-harmonics -- Complexity of network connectivity promotes self-organized criticality in cortical ensembles -- From neurons to networks: critical slowing down governs information processing across vigilance states -- The challenge of taming a latching network near criticality -- Fading memory, plasticity, and criticality in recurrent networks -- Homeostatic structural plasticity can build critical networks -- Investigating Linear Stability and Criticality in Local Cortical Circuits from Multi-Unit Activity -- Optimal Readout of Neural Activity Near Criticality -- Critical behavior and memory function in a model of spiking neurons with a reservoir of spatio-temporal patterns -- Assessing criticality in experiments -- The role of criticality in flexible visual information processing -- Statistical models of neural activity, criticality, and Zipf’s law. .
This book offers a timely overview of theories and methods developed by an authoritative group of researchers to understand the link between criticality and brain functioning. Cortical information processing in particular and brain function in general rely heavily on the collective dynamics of neurons and networks distributed over many brain areas. A key concept for characterizing and understanding brain dynamics is the idea that networks operate near a critical state, which offers several potential benefits for computation and information processing. However, there is still a large gap between research on criticality and understanding brain function. For example, cortical networks are not homogeneous but highly structured, they are not in a state of spontaneous activation but strongly driven by changing external stimuli, and they process information with respect to behavioral goals. So far the questions relating to how critical dynamics may support computation in this complex setting, and whether they can outperform other information processing schemes remain open. Based on the workshop “Dynamical Network States, Criticality and Cortical Function", held in March 2017 at the Hanse Institute for Advanced Studies (HWK) in Delmenhorst, Germany, the book provides readers with extensive information on these topics, as well as tools and ideas to answer the above-mentioned questions. It is meant for physicists, computational and systems neuroscientists, and biologists.
Neurosciences.
Computational intelligence.
Statistical physics.
Artificial intelligence.
Neurosciences.
Computational Intelligence.
Applications of Nonlinear Dynamics and Chaos Theory.
Artificial Intelligence.