About this Research Topic
The interplay between computational neuroscience and bio-inspired machine learning is gaining considerable momentum in our quest for advanced algorithms. Drawing inspiration from neuronal connectivity, synaptic plasticity, and the remarkable efficiency of information processing in biological systems, we can aspire to augment the learning capabilities of our artificial systems. This interdisciplinary approach holds the promise of advancements that are both theoretically intriguing and practically beneficial.
In this Research Topic, we aim to consolidate and further the dialogue between computational neuroscience and machine learning. We will delve into the intricacies of conceptualizing and implementing algorithms that take after the biological processes. We aim to question and bridge the dichotomy of biological learning and artificial learning, exploring how we can design artificial systems to extract patterns, learn from input, and evolve abilities just as efficiently as our neural circuitry does. By amalgamating the expertise of neuroscientists and machine learning researchers, we aim to accelerate the advent of compelling, bio-inspired machine learning architectures.
We encourage submissions from those working on the convergence of neuroscience and machine learning. We are particularly interested in works that apply neurobiological structures, mechanisms, and principles to the development of machine learning models.
Potential sub-themes could encompass:
- Advancements in biologically-inspired neural networks
- Novel paradigms for bio-inspired signal and pattern processing
- Emulation of efficient biological learning in artificial systems
- Innovations in optimizing computational costs for machine learning
- Challenges and limitations
- Methodological approaches
- Interdisciplinary collaboration
- Evaluation and metrics
We welcome Research Articles, Review Articles, and Brief Research Reports that highlight and discuss cutting-edge research in this field. Only original contributions will be considered, and all content must be articulated in a manner that is appropriate for an audience proficient in computational neuroscience and machine learning.
In this Research Topic, we aim to consolidate and further the dialogue between computational neuroscience and machine learning. We will delve into the intricacies of conceptualizing and implementing algorithms that take after the biological processes. We aim to question and bridge the dichotomy of biological learning and artificial learning, exploring how we can design artificial systems to extract patterns, learn from input, and evolve abilities just as efficiently as our neural circuitry does. By amalgamating the expertise of neuroscientists and machine learning researchers, we aim to accelerate the advent of compelling, bio-inspired machine learning architectures.
We encourage submissions from those working on the convergence of neuroscience and machine learning. We are particularly interested in works that apply neurobiological structures, mechanisms, and principles to the development of machine learning models.
Potential sub-themes could encompass:
- Advancements in biologically-inspired neural networks
- Novel paradigms for bio-inspired signal and pattern processing
- Emulation of efficient biological learning in artificial systems
- Innovations in optimizing computational costs for machine learning
- Challenges and limitations
- Methodological approaches
- Interdisciplinary collaboration
- Evaluation and metrics
We welcome Research Articles, Review Articles, and Brief Research Reports that highlight and discuss cutting-edge research in this field. Only original contributions will be considered, and all content must be articulated in a manner that is appropriate for an audience proficient in computational neuroscience and machine learning.
Keywords: machine learning, connectivity, synaptic plasticity, information processing, bio-inspired
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.
