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The cerebellum plays a critical role for sensorimotor control and learning. However, dysmetria or delays in movements’ onsets consequent to damages in cerebellum cannot be cured completely at the moment. Neuroprosthesis is an emerging technology that can potentially substitute such motor control module in the brain. A pre-requisite for this to become practical is the capability to simulate the cerebellum...
Sensorimotor control and learning require the function of sophisticated neural system. Cerebellum is one such brain region which comprises more than half of the total neuron population in the entire brain. Capable of simulating a bio-realistic cerebellum model provides important information for neuroscience and engineering. Here we present a Network-on-Chip (NoC) hardware architecture for implementing...
The cerebellum plays a critical role for sensorimotor control and learning. However dysmertria or delays in movements' onsets consequent to damages in cerebellum cannot be cured completely at the moment. To foster a potential cure based on neuroprosthetic technology, we present a frame-based Network-on-Chip (NoC) hardware architecture for implementing a bio-realistic cerebellum model with 100,000...
Hybrid bio-silicon networks are difficult to implement in practice due to variations of biological neuron bursting frequency. This causes the hybrid network to have inaccuracies and unreliability. The network may produce irregular bursts or incorrect spiking phase relationships if the electrical neuron bursting frequency is not suitable for biological neurons. To solve this potentially vital problem,...
The design of systems to replicate complex neural functionality is a requirement for the development of next-generation prosthetic devices. The demands of such neural models are growing exponentially as we discover more about how brain systems function. It is therefore important for the electronic architectures involved to scale effectively in terms of latency, area and power usage in order to be...
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