Brain function and pathology emerge from coordinated interactions within an extremely high-dimensional system which can be described at different hierarchical levels, from individual neurons and cell ensembles all the way to entire brain regions. The knowledge of how a single neuron behaves is insufficient to understanding how the brain encodes external stimuli, performs decision-making, forms memories, or how brain functions are disrupted in neurological disorders.
Reciprocal feedback between theories and experiments is essential for advancing understanding of the brain at the high-dimensional, network level. As experiments advance toward measuring more neurons simultaneously, the imperative grows for theories to help make sense of these extremely high-dimensional datasets. Without proper dimensionality reduction, it is impossible to distill useful information from the highly parallel recordings.
Likewise, the design of new experiments is improved by principled predictions from network-level theories, and experimental verification is valuable for theorists to improve their models and theories. There is also a pressing need to advance beyond traditional low-dimensional experiments and theories, which measure and explain how a small number of neurons are involved in a single function. Moreover, it is important to reconcile new high-dimensional measurements with well-studied lower dimensional population-level signals like local-field potentials, electroencephalography, and functional magnetic resonance imaging.
The goal of this Research Topic is to highlight interactive work among experimentalists, modelers, and theorists. We welcome submissions in the form of original research articles, methodological advances, reviews, and perspectives on new techniques to record and analyze population neuronal activity, theoretical and computational approaches to understand neuronal population dynamics, and alterations of population activity in abnormal conditions.
Specific themes include, but are not limited to, the following:
• Dimensionality reduction of population spiking and field potential data,
• Population coding,
• Balanced network dynamics,
• Criticality,
• Structure of ongoing activity,
• Attractor states,
• Oscillations,
• Effects of perturbation on the structure of population activity.
We are particularly interested in experimental studies that address specific theoretical hypotheses, and computational work that makes experimentally falsifiable predictions.
Brain function and pathology emerge from coordinated interactions within an extremely high-dimensional system which can be described at different hierarchical levels, from individual neurons and cell ensembles all the way to entire brain regions. The knowledge of how a single neuron behaves is insufficient to understanding how the brain encodes external stimuli, performs decision-making, forms memories, or how brain functions are disrupted in neurological disorders.
Reciprocal feedback between theories and experiments is essential for advancing understanding of the brain at the high-dimensional, network level. As experiments advance toward measuring more neurons simultaneously, the imperative grows for theories to help make sense of these extremely high-dimensional datasets. Without proper dimensionality reduction, it is impossible to distill useful information from the highly parallel recordings.
Likewise, the design of new experiments is improved by principled predictions from network-level theories, and experimental verification is valuable for theorists to improve their models and theories. There is also a pressing need to advance beyond traditional low-dimensional experiments and theories, which measure and explain how a small number of neurons are involved in a single function. Moreover, it is important to reconcile new high-dimensional measurements with well-studied lower dimensional population-level signals like local-field potentials, electroencephalography, and functional magnetic resonance imaging.
The goal of this Research Topic is to highlight interactive work among experimentalists, modelers, and theorists. We welcome submissions in the form of original research articles, methodological advances, reviews, and perspectives on new techniques to record and analyze population neuronal activity, theoretical and computational approaches to understand neuronal population dynamics, and alterations of population activity in abnormal conditions.
Specific themes include, but are not limited to, the following:
• Dimensionality reduction of population spiking and field potential data,
• Population coding,
• Balanced network dynamics,
• Criticality,
• Structure of ongoing activity,
• Attractor states,
• Oscillations,
• Effects of perturbation on the structure of population activity.
We are particularly interested in experimental studies that address specific theoretical hypotheses, and computational work that makes experimentally falsifiable predictions.
