Lecture

Neural Interfaces: Course Overview

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In course

NX-422: Neural interfaces

Neural interfaces (NI) are bioelectronic systems that interface the nervous system to digital technologies. This course presents their main building blocks (transducers, instrumentation & communication), reviews current and upcoming materials and technolog ...

Description

This lecture introduces the concept of neural interfaces, covering topics such as encoding and decoding neural information, course organization, weekly lectures, TA sessions, course projects, and grading criteria. It emphasizes the importance of interdisciplinary knowledge in designing and engineering neural interfaces.

Instructors (2)

Mahsa Shoaran

Stéphanie Lacour

Stéphanie P. Lacour holds the Bertarelli Foundation Chair in Neuroprosthetic Technology in the School of Engineering at EPFL and leads the Laboratory for Soft Bioelectronic Interfaces. She received her PhD in Electrical Engineering from INSA de Lyon, France, and completed postdoctoral research at Princeton University and the University of Cambridge. She is the recipient of the 2006 MIT TR35, a University Research Fellowship of the Royal Society, European Research Council ERC Starting and POC Grants, and a SNSF-ERC Consolidator Grant. She was elected a 2015 Young Global Leader by the World Economic Forum.

Official source

Related concepts (18)

Neural coding

Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is thought that neurons can encode both digital and analog information.

Neural decoding

Neural decoding is a neuroscience field concerned with the hypothetical reconstruction of sensory and other stimuli from information that has already been encoded and represented in the brain by networks of neurons. Reconstruction refers to the ability of the researcher to predict what sensory stimuli the subject is receiving based purely on neuron action potentials. Therefore, the main goal of neural decoding is to characterize how the electrical activity of neurons elicit activity and responses in the brain.

Brain–computer interface

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine.

Neuronal ensemble

A neuronal ensemble is a population of nervous system cells (or cultured neurons) involved in a particular neural computation. The concept of neuronal ensemble dates back to the work of Charles Sherrington who described the functioning of the CNS as the system of reflex arcs, each composed of interconnected excitatory and inhibitory neurons. In Sherrington's scheme, α-motoneurons are the final common path of a number of neural circuits of different complexity: motoneurons integrate a large number of inputs and send their final output to muscles.

Spiking neural network

Artificial neural network Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold.

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