Neural Interfaces: Optogenetics and Temperature Sensing

In course

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)

Official source

https://mediaspace.epfl.ch/media/0_vifgy03e

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts (38)

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.

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.

LED street light

An LED street light or road light is an integrated light-emitting diode (LED) light fixture that is used for street lighting. An LED street light is an integrated light that uses light emitting diodes (LED) as its light source. These are considered integrated lights because, in most cases, the luminaire and the fixture are not separate parts. In manufacturing, the LED light cluster is sealed on a panel and then assembled to the LED panel with a heat sink to become an integrated lighting fixture.