The use of High Performance Computing (HPC) technologies is gaining interest in the field of neuronal activity simulations. In fact, scientists’ main goal is to understand and reproduce cells behavior in a realistic way. This will allow undertaking in silico experiments, instead of in vivo ones, to test new medicines, to study cerebral pathologies and to discover innovative therapies. To this aim, two main requirements are necessary: neurons have to be described by realistic models and their simulation hopefully have to satisfy the real-time constraint. This last property is very hard to accommodate because models used in these works are very heavy from the computational point of view. For this reason, authors decide to exploit Graphic Pro...
Compartmental modeling is a widely used tool in neurophysiology but the detail and scope of such mod...
Over the past decade there has been a growing interest in the development of parallel hardware syste...
Current brain simulators do no scale linearly to realistic problem sizes (e.g. >100,000 neurons),...
The use of High Performance Computing (HPC) technologies is gaining interest in the field of neurona...
Realistic neuronal activity simulation is of central importance for neuroscientists. These simulatio...
High performance computing (HPC) is becoming mandatory for the simulation of complex and realistic n...
In modern computational modeling, neuroscientists need to reproduce long-lasting activity of large-s...
Studying and understanding human brain is one of the main challenges of 21st century scientists. Th...
Large-scale simulation of detailed computational models of neuronal microcircuits plays a prominent ...
Understanding and emulating the brain behaviour is one of the most challenging topic in neuroscience...
Large-scale simulations of parts of the brain using detailed neuronal models to improve our understa...
The human brain is an incredible system which can process, store, and transfer information with high...
The human brain is an incredible system which can process, store, and transfer information with high...
This work presents the first simulation of a large-scale, bio-physically constrained cerebellum mode...
Simulating biological neural networks is an important task for computational neuroscientists attempt...
Compartmental modeling is a widely used tool in neurophysiology but the detail and scope of such mod...
Over the past decade there has been a growing interest in the development of parallel hardware syste...
Current brain simulators do no scale linearly to realistic problem sizes (e.g. >100,000 neurons),...
The use of High Performance Computing (HPC) technologies is gaining interest in the field of neurona...
Realistic neuronal activity simulation is of central importance for neuroscientists. These simulatio...
High performance computing (HPC) is becoming mandatory for the simulation of complex and realistic n...
In modern computational modeling, neuroscientists need to reproduce long-lasting activity of large-s...
Studying and understanding human brain is one of the main challenges of 21st century scientists. Th...
Large-scale simulation of detailed computational models of neuronal microcircuits plays a prominent ...
Understanding and emulating the brain behaviour is one of the most challenging topic in neuroscience...
Large-scale simulations of parts of the brain using detailed neuronal models to improve our understa...
The human brain is an incredible system which can process, store, and transfer information with high...
The human brain is an incredible system which can process, store, and transfer information with high...
This work presents the first simulation of a large-scale, bio-physically constrained cerebellum mode...
Simulating biological neural networks is an important task for computational neuroscientists attempt...
Compartmental modeling is a widely used tool in neurophysiology but the detail and scope of such mod...
Over the past decade there has been a growing interest in the development of parallel hardware syste...
Current brain simulators do no scale linearly to realistic problem sizes (e.g. >100,000 neurons),...