About this Research Topic
As the interplay between Computational Neuroscience and Artificial intelligence intensifies, conventional boundaries dissipate, leading to the emergence of machine learning techniques as pivotal instruments for addressing issues erstwhile solved through traditional signal processing approaches. Enriching this confluence is the potential to enhance the efficacy of decoding, modeling, and prediction in the neuroscience sphere.
This Research Topic welcomes contributions exploring in the deployment of machine learning for signal processing within the field of computational neuroscience. We encourage researchers to share their latest findings and crucial insights that do justice to the dynamism of this scientific intersectionality.
The canvases for exploration include, but are not limited to, the following:
• Integrating signal processing and machine learning for brain-computer interfaces and sensor networks highlighting how these combined methods can optimize our decoding of neural data.
• Modelling the learning brain, deepening understanding of how learning theories and computational models can guide the development of more efficient algorithms and aid in better prediction of neurobiological data.
• Neural networks and deep learning in computational neuroscience, providing profound insight into the use of machine learning tools for modeling complex neural systems.
• Probabilistic brain models, Bayesian learning and its role in developing models that can predict brain responses in various conditions.
• Sequential learning, sequential decision methods in neural coding
• Information-theoretic approaches to understanding neural data:
• Graphical and kernel models for brain network analysis, reporting advances in the usage of these models for understanding brain network structure and dynamics.
• Sharing insights into the validation of computational models against empirical neuroscience data.
• Signal detection, pattern recognition, and classification in neural data
• High-dimensional neural data analysis
• Machine learning for big neuroscience data
• Unsupervised and semi-supervised learning in neurobiology
• Active and reinforcement learning in simulated neural systems
• Multi-modal neuroimaging
This Research Topic welcomes contributions exploring in the deployment of machine learning for signal processing within the field of computational neuroscience. We encourage researchers to share their latest findings and crucial insights that do justice to the dynamism of this scientific intersectionality.
The canvases for exploration include, but are not limited to, the following:
• Integrating signal processing and machine learning for brain-computer interfaces and sensor networks highlighting how these combined methods can optimize our decoding of neural data.
• Modelling the learning brain, deepening understanding of how learning theories and computational models can guide the development of more efficient algorithms and aid in better prediction of neurobiological data.
• Neural networks and deep learning in computational neuroscience, providing profound insight into the use of machine learning tools for modeling complex neural systems.
• Probabilistic brain models, Bayesian learning and its role in developing models that can predict brain responses in various conditions.
• Sequential learning, sequential decision methods in neural coding
• Information-theoretic approaches to understanding neural data:
• Graphical and kernel models for brain network analysis, reporting advances in the usage of these models for understanding brain network structure and dynamics.
• Sharing insights into the validation of computational models against empirical neuroscience data.
• Signal detection, pattern recognition, and classification in neural data
• High-dimensional neural data analysis
• Machine learning for big neuroscience data
• Unsupervised and semi-supervised learning in neurobiology
• Active and reinforcement learning in simulated neural systems
• Multi-modal neuroimaging
Keywords: Smart signal processing, machine learning, medical signal processing, neurosciences, artificial intelligence
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.