Lecture

Neural Engineering: Fundamentals and Applications

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In course

BIOENG-448: Fundamentals of neuroengineering

Neuroengineering is at the frontier between neuroscience and engineering: understanding how the brain works allows developing engineering applications and therapies of high impact, while the design of new measurement and data analysis techniques contribute ...

Description

This lecture introduces the field of neural engineering, which combines biomedical engineering with neuroscience to understand, repair, and enhance neural systems. It covers topics such as computational neuroscience, brain-computer interfaces, neuroprosthetics, and deep brain stimulation. The course explores the design of neurotechnologies for various applications, including sensory-motor neuroprostheses and bioelectronic medicine. Students will learn about methodologies for processing neural and muscular signals, developing computational models, and enhancing human function through direct interactions with artificial devices.

Instructors (2)

Solaiman Shokur

2019 - now : Senior Scientist, Translational neuroengineering Laboratory, EPFL(Geneva) Team leader: CHRONOS project, a multi-center European project that aims at developing the first chronically implanted prosthetic hand for transradial amputee patients with bidirectional communication capabilities. NCCR Robotics: Bi-directional control of supernumerary limbs.2014-2019: Research Coordinator, AASDAP (São Paulo, Brazil) Responsible for both the scientific production and the clinical protocol at the AASDAP neurorehabilitation laboratory. 2013-2014: Postdoctoral associate, Walk again project, Insituto Santos Dumond. (Natal, RN, Brazil)Head of the engineering team in charge of the system integration. 2010-2012: Visiting scientist, Nicolelis Lab (Duke University). Development and validation of a virtual-reality based brain-machine interface for rhesus monkeys. 2007 – 2010: Teaching assistant, Laboratory of Robotics Systems (EPFL)

Silvestro Micera

Short biographical sketch Silvestro Micera received the University degree (Laurea) in Electrical Engineering from the University of Pisa, in 1996, and the Ph.D. degree in Biomedical Engineering from the Scuola Superiore Sant'Anna, in 2000. From 2000 to 2009, he has been an Assistant Professor of BioRobotics at the Scuola Superiore Sant'Anna where he is now the Head of the Neural Engineering group. In 2007 he was a Visiting Scientist at the Massachusetts Institute of Technology, Cambridge, USA with a Fulbri

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Related concepts (32)

Artificial neural network

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.

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Brain implant

Brain implants, often referred to as neural implants, are technological devices that connect directly to a biological subject's brain – usually placed on the surface of the brain, or attached to the brain's cortex. A common purpose of modern brain implants and the focus of much current research is establishing a biomedical prosthesis circumventing areas in the brain that have become dysfunctional after a stroke or other head injuries. This includes sensory substitution, e.g., in vision.

Computational neuroscience

Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematical models, computer simulations, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system. Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous.

Deep brain stimulation

Deep brain stimulation (DBS) is a neurosurgical procedure involving the placement of a medical device called a neurostimulator, which sends electrical impulses, through implanted electrodes, to specific targets in the brain (the brain nucleus) for the treatment of movement disorders, including Parkinson's disease, essential tremor, dystonia, and other conditions such as obsessive-compulsive disorder (OCD) and epilepsy. While its underlying principles and mechanisms are not fully understood, DBS directly changes brain activity in a controlled manner.

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