About this Research Topic
Neural activity coordination underlies the higher functions of the brain. Action planning, sensation, perception, and cognition are thought to depend on the spatial and temporal organization of neuronal firing. The role of the spiking activities across the brain and how the information is coded, are still debated.
Up to now, a range of biophysical and physiological models have been developed aiming to understand the brain connectivity from firing activity. The major focus of these models are to provide an explanation on how cognitive and behavioral processes are planned and performed. How information is transmitted from the single-neuron level to the neural network organization is still a matter of discussion. These models have already provided insight into cognitive and behavioral disorders such as Parkinson’s disease. The model-driven approach is complemented by a data-driven approach, and deeper insights may be achieved when both are integrated. Data sources include EEG, ERP, MRI, PET, and so on.
Among the different theories that have been studied, the ones rooted in the complexity theory are the most discussed at the moment. Some researchers interpreted the nonlinear activity of neurons and their collective action as being functionally significant, rather than noisy linear signals.
Until now, there haven’t been any clear breakthroughs linking mathematical computation with complex neuronal models, and the corresponding biological data. This Research Topic welcomes contributions from neuroscientists, biophysicists, engineers, mathematicians and cognitive scientists, to review the current state-of-the-art of complex neural models and associated datasets.
We aim to collect novel ideas around (but not limited to) the research field subtopics below to study new theoretical and empirical neural models:
1. Feature extraction and classification in physiological biometrics
2. Emotion classification using nonlinear EEG analysis
3. Detection of epileptic seizures using MRI
4. Alzheimer’s and Parkinson’s Disease identification, including cognitive impairment
5. Mental workload assessment and EEG dynamics
6. Developmental disorder assessment, including ADHD and autism
7. Controlling the Brain-Computer Interface (BCI)
8. Measures of neuroplasticity and neural complexity
9. Effects of physical activity and exercise on brain dynamics and cognitive function
10. Brain dynamics and neuronal interactions during the sleep-wake cycle and under sleep disorders
11. Neuronal dynamics and emergence of cortical rhythms
12. Criticality and self-organization in neuronal populations and brain dynamics
Up to now, a range of biophysical and physiological models have been developed aiming to understand the brain connectivity from firing activity. The major focus of these models are to provide an explanation on how cognitive and behavioral processes are planned and performed. How information is transmitted from the single-neuron level to the neural network organization is still a matter of discussion. These models have already provided insight into cognitive and behavioral disorders such as Parkinson’s disease. The model-driven approach is complemented by a data-driven approach, and deeper insights may be achieved when both are integrated. Data sources include EEG, ERP, MRI, PET, and so on.
Among the different theories that have been studied, the ones rooted in the complexity theory are the most discussed at the moment. Some researchers interpreted the nonlinear activity of neurons and their collective action as being functionally significant, rather than noisy linear signals.
Until now, there haven’t been any clear breakthroughs linking mathematical computation with complex neuronal models, and the corresponding biological data. This Research Topic welcomes contributions from neuroscientists, biophysicists, engineers, mathematicians and cognitive scientists, to review the current state-of-the-art of complex neural models and associated datasets.
We aim to collect novel ideas around (but not limited to) the research field subtopics below to study new theoretical and empirical neural models:
1. Feature extraction and classification in physiological biometrics
2. Emotion classification using nonlinear EEG analysis
3. Detection of epileptic seizures using MRI
4. Alzheimer’s and Parkinson’s Disease identification, including cognitive impairment
5. Mental workload assessment and EEG dynamics
6. Developmental disorder assessment, including ADHD and autism
7. Controlling the Brain-Computer Interface (BCI)
8. Measures of neuroplasticity and neural complexity
9. Effects of physical activity and exercise on brain dynamics and cognitive function
10. Brain dynamics and neuronal interactions during the sleep-wake cycle and under sleep disorders
11. Neuronal dynamics and emergence of cortical rhythms
12. Criticality and self-organization in neuronal populations and brain dynamics
Keywords: EEG, ECG, ECoG, Complexity, Chaos, Nonlinear systems, Neural and cognitive models
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
