About this Research Topic
Neuronal communication is highly interconnected, involving various neuron types and subtypes, and is further modulated by astrocytes. These interactions are compartmentalized through synapses, dendrites, soma, and axons. Recent advancements in bio-inspired algorithms and hardware that mimic the multi-scalar and spatial principles of the biological nervous system have opened new avenues for research. These developments have significant implications for fundamental knowledge in neuronal signaling, biomedical applications, neural networks, and neuromorphic computational and hardware advances.
Despite these advancements, there remain significant gaps in our understanding of the regional and spatial complexity of these bio-inspired methods, particularly in mapping the neural connectivity of the human brain. Addressing these gaps is crucial for understanding aging, trauma, and brain pathologies that impact neural plasticity and cognition. More sophisticated experiments are needed to elucidate the specific features of astrocytes in different brain regions and layers, considering the cellular heterogeneity that likely influences astrocyte-neuron signaling outcomes.
This research topic aims to collect novel and high-quality research articles that investigate methods and applications in neuronal signaling for fundamental knowledge, biomedical applications, neural nets, and neuromorphic computational and hardware advances. Specifically, we seek original contributions related to modeling studies and algorithm development, hardware design applications, and lab-based experimental results in the different areas of brain neural communication. By providing experimentally testable predictions that range from the subcellular level to the systems level, this research topic aims to drive forward our understanding of brain neural communication and its relevance to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modeling predictions often necessitates new technological advances, thereby enhancing the feedback loop among modeling studies, hardware design applications, and lab-based experimentation.
To gather further insights into the spatial and regional mapping of brain neural communication, we welcome articles addressing, but not limited to, the following themes:
- Dendritic mapping of neuronal communication (dendritic spikes, subunits conductance, etc.)
- Graphical neural nets (regional mapping of neural and neuronal types/subtypes) and functional analysis
- Multi-synaptic input to dendrites (excitatory, inhibitory, etc.)
- Plasticity modeling of single or multiple synapses
- Bio-inspired deep learning for medical imaging of the Central Nervous System (CNS)
- Neuromorphic hardware design applications for brain neural communications
Despite these advancements, there remain significant gaps in our understanding of the regional and spatial complexity of these bio-inspired methods, particularly in mapping the neural connectivity of the human brain. Addressing these gaps is crucial for understanding aging, trauma, and brain pathologies that impact neural plasticity and cognition. More sophisticated experiments are needed to elucidate the specific features of astrocytes in different brain regions and layers, considering the cellular heterogeneity that likely influences astrocyte-neuron signaling outcomes.
This research topic aims to collect novel and high-quality research articles that investigate methods and applications in neuronal signaling for fundamental knowledge, biomedical applications, neural nets, and neuromorphic computational and hardware advances. Specifically, we seek original contributions related to modeling studies and algorithm development, hardware design applications, and lab-based experimental results in the different areas of brain neural communication. By providing experimentally testable predictions that range from the subcellular level to the systems level, this research topic aims to drive forward our understanding of brain neural communication and its relevance to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modeling predictions often necessitates new technological advances, thereby enhancing the feedback loop among modeling studies, hardware design applications, and lab-based experimentation.
To gather further insights into the spatial and regional mapping of brain neural communication, we welcome articles addressing, but not limited to, the following themes:
- Dendritic mapping of neuronal communication (dendritic spikes, subunits conductance, etc.)
- Graphical neural nets (regional mapping of neural and neuronal types/subtypes) and functional analysis
- Multi-synaptic input to dendrites (excitatory, inhibitory, etc.)
- Plasticity modeling of single or multiple synapses
- Bio-inspired deep learning for medical imaging of the Central Nervous System (CNS)
- Neuromorphic hardware design applications for brain neural communications
Keywords: Dendritic mapping, Graphical neural nets, Excitatory and Inhibitory Synapses, Plasticity, AI, Neuromorphic, Hardware and Software
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.
