Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

A classical view of neural computation is that it can be characterized in terms of convergence to attractor states or sequential transitions among states in a noisy background. After over three decades, is this still a valid model of how brain dynamics implements cognition? This book provides a comprehensive collection of recent theoretical and experimental contributions addressing the question of stable versus transient neural population dynamics from complementary angles. These studies showcase recent efforts for designing a framework that encompasses the multiple facets of metastability in neural responses, one of the most exciting topics currently in systems and computational neuroscience.

Attractor Dynamics --- Trial-to-trial Variability --- Transient Dynamics --- Neural Noise --- Metastability --- Attractor Dynamics --- Trial-to-trial Variability --- Transient Dynamics --- Neural Noise --- Metastability

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

A classical view of neural computation is that it can be characterized in terms of convergence to attractor states or sequential transitions among states in a noisy background. After over three decades, is this still a valid model of how brain dynamics implements cognition? This book provides a comprehensive collection of recent theoretical and experimental contributions addressing the question of stable versus transient neural population dynamics from complementary angles. These studies showcase recent efforts for designing a framework that encompasses the multiple facets of metastability in neural responses, one of the most exciting topics currently in systems and computational neuroscience.

Attractor Dynamics --- Trial-to-trial Variability --- Transient Dynamics --- Neural Noise --- Metastability