Lecture

Hybrid Neural Models: Introduction 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 hybrid neural models for neuroprosthetics, covering topics such as nerve stimulation optimization, grasping restoration after spinal cord injury, and computational modeling for defining implantation sites. The instructor discusses the design of implantable systems, sensory loop control, and the restoration of grasping and locomotion using epidural electrical stimulation.

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

Official source

Related concepts (32)

Neural engineering

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.

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.

Spinal cord stimulator

A spinal cord stimulator (SCS) or dorsal column stimulator (DCS) is a type of implantable neuromodulation device (sometimes called a "pain pacemaker") that is used to send electrical signals to select areas of the spinal cord (dorsal columns) for the treatment of certain pain conditions. SCS is a consideration for people who have a pain condition that has not responded to more conservative therapy. There are also spinal cord stimulators under research and development that could enable patients with spinal cord injury to walk again via epidural electrical stimulation (EES).

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.

Nerve

A nerve is an enclosed, cable-like bundle of nerve fibers (called axons) in the peripheral nervous system. Axons transmit electrical impulses. Nerves have historically been considered the basic units of the peripheral nervous system. A nerve provides a common pathway for the electrochemical nerve impulses called action potentials that are transmitted along each of the axons to peripheral organs or, in the case of sensory nerves, from the periphery back to the central nervous system.

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Electrical Stimulation for Actuation

Covers techniques for restoring motor functions through electrical stimulation, including electrode types and applications, muscle fatigue challenges, and epidural spinal cord stimulation for intentional movement restoration.

Neural Signals and Signal Processing: Modeling and Simulation

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