Neural circuits in the brain are highly sophisticated biological systems, orchestrating ultra-fast, parallel computations that integrate sensory inputs, motor outputs, and internal memory processes. Notably, biological neural circuits can process signals extremely efficiently with far less energy consumption compared to conventional digital digital computing. Unraveling the mechanisms behind this energy-efficient computation is a highly compelling frontier in neuroscience research.
This Research Topic aims to foster a multidisciplinary dialogue that bridges gaps and catalyzes collaborations among neuroscientists from diverse fields. We seek to facilitate a mutual understanding between in silico, in vitro, and in vivo researchers, overcoming the historical barriers posed by limited information exchange. Contributions are invited across a spectrum of disciplines, including computational neuroscience, biophysics, neurophysiology, materials science, neuromorphic hardware, bioengineering, and medicine.
Our focus extends to the multifaceted analysis of multicellular computation within neural networks. We encourage submissions that leverage mathematical modeling to dissect these complex interactions, as well as those that contribute to the evolution of advanced brain-inspired computing systems. This includes research utilizing both analog and digital hardware, supported by innovative materials science, and the design of cultured neuronal networks with specified geometries and complexities.
We welcome submissions from researchers dedicated to advancing computational models and devices that emulate multiple functional aspects of brain function, drawing inspiration from neurobiological principles. Additionally, this collection encompasses research aimed at understanding and manipulating neural circuits in both physiological and pathological states, offering insights into the neural basis of health and disease.
In this interdisciplinary endeavor, we aspire to push the boundaries of our understanding of neural circuitry, paving the way for the next generation of computational models and technologies that mirror the brain's unparalleled capabilities.
Neural circuits in the brain are highly sophisticated biological systems, orchestrating ultra-fast, parallel computations that integrate sensory inputs, motor outputs, and internal memory processes. Notably, biological neural circuits can process signals extremely efficiently with far less energy consumption compared to conventional digital digital computing. Unraveling the mechanisms behind this energy-efficient computation is a highly compelling frontier in neuroscience research.
This Research Topic aims to foster a multidisciplinary dialogue that bridges gaps and catalyzes collaborations among neuroscientists from diverse fields. We seek to facilitate a mutual understanding between in silico, in vitro, and in vivo researchers, overcoming the historical barriers posed by limited information exchange. Contributions are invited across a spectrum of disciplines, including computational neuroscience, biophysics, neurophysiology, materials science, neuromorphic hardware, bioengineering, and medicine.
Our focus extends to the multifaceted analysis of multicellular computation within neural networks. We encourage submissions that leverage mathematical modeling to dissect these complex interactions, as well as those that contribute to the evolution of advanced brain-inspired computing systems. This includes research utilizing both analog and digital hardware, supported by innovative materials science, and the design of cultured neuronal networks with specified geometries and complexities.
We welcome submissions from researchers dedicated to advancing computational models and devices that emulate multiple functional aspects of brain function, drawing inspiration from neurobiological principles. Additionally, this collection encompasses research aimed at understanding and manipulating neural circuits in both physiological and pathological states, offering insights into the neural basis of health and disease.
In this interdisciplinary endeavor, we aspire to push the boundaries of our understanding of neural circuitry, paving the way for the next generation of computational models and technologies that mirror the brain's unparalleled capabilities.