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Lecture

Neural Interfaces: Optogenetics and Temperature Sensing

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 covers the challenges and techniques involved in using multimodal probes for neural interfaces, including embedding opsins, optogenetics for probing circuits, controlled induced heating, sensing temperature with metallic thermistors, and in situ temperature sensors. The instructor discusses the importance of miniaturisation, hybrid integration of LEDs, power and thermal management, packaging, and long-term performance.

Instructors (2)

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.

Mahsa Shoaran

Official source

Related concepts (23)

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.

Light-emitting diode

A light-emitting diode (LED) is a semiconductor device that emits light when current flows through it. Electrons in the semiconductor recombine with electron holes, releasing energy in the form of photons. The color of the light (corresponding to the energy of the photons) is determined by the energy required for electrons to cross the band gap of the semiconductor. White light is obtained by using multiple semiconductors or a layer of light-emitting phosphor on the semiconductor device.

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.

LED lamp

An LED lamp or LED light is an electric light that produces light using light-emitting diodes (LEDs). LED lamps are significantly more energy-efficient than equivalent incandescent lamps and can be significantly more than most fluorescent lamps. The most efficient commercially available LED lamps have efficiencies exceeding 200 lumens per watt (lm/W) and convert more than half the input power into light. Commercial LED lamps have a lifespan several times longer than both incandescent and fluorescent lamps.

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

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