Unlike other fields of biology, neuroscience and the study of the nervous system across a range of species were characterized early on by the need for the analysis of highly complex data sets. In seminal areas of neuroscience, such as electrophysiology, neuroimaging and neurophysiology, progress in both data acquisition and data analysis was limited by capabilities of hardware and software solutions available to researchers. At the same time, critical advances in computer science and software engineering not only led to fundamentally important new discoveries in neuroscience, but were often also driven by challenges resulting from paradigm shifting discoveries in neuroscience, such as the 1981 Nobel Prize in Physiology or Medicine. Similar synergistic relationships between computer science and neuroscience exist also in medical fields related to neuroscience, such as neurology, ophthalmology and psychiatry, where progress in the development of novel therapeutics and diagnostics depends on the ability to more effectively acquire and analyze data, such as the 2003 Nobel Prize in Physiology or Medicine.
The seminal importance of many novel developments in computer science generates broad impact on basic science approaches in neuroscience, often subsequently or in parallel advancing towards clinical implementation. Novel approaches in the areas of artificial intelligence and machine learning, for example, have found early sites of implementation in neuroscience and related medical fields. At the same time, developments in neuroscience, such as international collaborative efforts in connectomics and brain–computer interface research, provide new concepts and prompt new challenges for computer scientist in fundamental and applied research fields. The goal of the present Research Topic is to provide a forum for experts in both areas and interdisciplinary teams to highlight their latest research, discuss interdisciplinary approaches and synthesize insights towards the synergistic advancement of their respective fields.
This Research Topic is accepting review articles and original papers covering recent developments and impactful innovation in the fields of
- computer science and software engineering informing data acquisition and analysis in neuroscience,
- neuroscience and related fields, such as neuroimaging and neurophysiology, informing innovation in computer science and software engineering,
- electrical and chemical engineering, molecular biology and chemistry providing paradigm shifts and critical innovation for how computer science and neuroscience synergistically inform their respective fields.
Articles can stem from a broad range of approaches in preclinical and translational research, as well as from clinical research in medical fields related to neuroscience, such as neurology, ophthalmology and psychiatry.
Unlike other fields of biology, neuroscience and the study of the nervous system across a range of species were characterized early on by the need for the analysis of highly complex data sets. In seminal areas of neuroscience, such as electrophysiology, neuroimaging and neurophysiology, progress in both data acquisition and data analysis was limited by capabilities of hardware and software solutions available to researchers. At the same time, critical advances in computer science and software engineering not only led to fundamentally important new discoveries in neuroscience, but were often also driven by challenges resulting from paradigm shifting discoveries in neuroscience, such as the 1981 Nobel Prize in Physiology or Medicine. Similar synergistic relationships between computer science and neuroscience exist also in medical fields related to neuroscience, such as neurology, ophthalmology and psychiatry, where progress in the development of novel therapeutics and diagnostics depends on the ability to more effectively acquire and analyze data, such as the 2003 Nobel Prize in Physiology or Medicine.
The seminal importance of many novel developments in computer science generates broad impact on basic science approaches in neuroscience, often subsequently or in parallel advancing towards clinical implementation. Novel approaches in the areas of artificial intelligence and machine learning, for example, have found early sites of implementation in neuroscience and related medical fields. At the same time, developments in neuroscience, such as international collaborative efforts in connectomics and brain–computer interface research, provide new concepts and prompt new challenges for computer scientist in fundamental and applied research fields. The goal of the present Research Topic is to provide a forum for experts in both areas and interdisciplinary teams to highlight their latest research, discuss interdisciplinary approaches and synthesize insights towards the synergistic advancement of their respective fields.
This Research Topic is accepting review articles and original papers covering recent developments and impactful innovation in the fields of
- computer science and software engineering informing data acquisition and analysis in neuroscience,
- neuroscience and related fields, such as neuroimaging and neurophysiology, informing innovation in computer science and software engineering,
- electrical and chemical engineering, molecular biology and chemistry providing paradigm shifts and critical innovation for how computer science and neuroscience synergistically inform their respective fields.
Articles can stem from a broad range of approaches in preclinical and translational research, as well as from clinical research in medical fields related to neuroscience, such as neurology, ophthalmology and psychiatry.
