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Lecture

Optimization of Neuroprosthetic Systems

In course

NX-421: Neural signals and signal processing

Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system ...

Description

This lecture covers the optimization of neuroprosthetic systems, focusing on the design of implantable systems, sensation encoding, nerve stimulation, robotic hand sensors, stimulation strategies, sensory feedback restoration, and sensory loop sensors. It also discusses the use of peripheral neural stimulation to restore sensory feedback and motor control, including epidural electrical stimulation for locomotion and reaching tasks, and deep brain stimulation.

Instructors (2)

Silvestro Micera

Dimitri Nestor Alice Van De Ville

Official source

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Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

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Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, or enhance neural systems. Neural engineers are uniquely qualified to solve design problems at the interface of living neural tissue and non-living constructs (Hetling, 2008). The field of neural engineering draws on the fields of computational neuroscience, experimental neuroscience, neurology, electrical engineering and signal processing of living neural tissue, and encompasses elements from robotics, cybernetics, computer engineering, neural tissue engineering, materials science, and nanotechnology.

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Untitled

Related concepts (32)

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Recurrent neural network