About this Research Topic
Here we present the Frontiers in Neuroscience ‘Emerging Talents in’ article collection. A series dedicated to highlighting the emerging talent of student researchers within the field of Neuromorphic Engineering.
Across the world students are undertaking key research as part of their education in Neuromorphic Engineering, however, most of this research is not communicated to the wider audience. We recognise that this is because many student researchers find the thought of peer-review daunting. At Frontiers, peer-review is considered a collaborative process and our interactive peer-review is tailored to provide hands-on guidance and constructive feedback to researchers. Our Topic Editors are committed to the development of emerging talents and want to see student researchers strive for success at publications.
The research presented here highlights the quality and diversity of student researchers across the field of Neuromorphic Engineering. We welcome contributions in the form of original research, review, mini review, case report, hypothesis and theory, perspective, both experimental and computational studies that cover, but are not limited to, following themes:
- Learning and inference with SNN in neuromorphic hardware
- Application of new technologies in the design of neuromorphic systems
- Applications of neuromorphic systems to robotics, brain-computer interfaces, biomedical signal processing, event-based processing, etc.
- Sensing with neuromorphic hardware
- Algorithms for neuromorphic hardware
- Implementation of bio-inspired models with SNN
- Application of spiking neural networks in neuromorphic systems
- Implementation of neural computational systems in non-human animals
- Identification of new potential applications for Neuromorphic Computing and Engineering
This Research Topic will help to identify emerging leaders and allow the community to follow the aspiring careers of our emerging, talented researchers.
Across the world students are undertaking key research as part of their education in Neuromorphic Engineering, however, most of this research is not communicated to the wider audience. We recognise that this is because many student researchers find the thought of peer-review daunting. At Frontiers, peer-review is considered a collaborative process and our interactive peer-review is tailored to provide hands-on guidance and constructive feedback to researchers. Our Topic Editors are committed to the development of emerging talents and want to see student researchers strive for success at publications.
The research presented here highlights the quality and diversity of student researchers across the field of Neuromorphic Engineering. We welcome contributions in the form of original research, review, mini review, case report, hypothesis and theory, perspective, both experimental and computational studies that cover, but are not limited to, following themes:
- Learning and inference with SNN in neuromorphic hardware
- Application of new technologies in the design of neuromorphic systems
- Applications of neuromorphic systems to robotics, brain-computer interfaces, biomedical signal processing, event-based processing, etc.
- Sensing with neuromorphic hardware
- Algorithms for neuromorphic hardware
- Implementation of bio-inspired models with SNN
- Application of spiking neural networks in neuromorphic systems
- Implementation of neural computational systems in non-human animals
- Identification of new potential applications for Neuromorphic Computing and Engineering
This Research Topic will help to identify emerging leaders and allow the community to follow the aspiring careers of our emerging, talented researchers.
Keywords: Emerging Talents, Neuromorphic Engineering, #CollectionSeries
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.
