Publication

Event-Driven Spike Encoding via Asymmetric Schottky Junction Photodiodes for Bioinspired Vision Systems

2026

Abstract

Conventional frame-based vision systems inevitably generate large amounts of redundant data owing to the continuous capture of absolute light intensity. Here, we present a bioinspired vision system that converts dynamic self-powered photocurrents into sparse event-driven spikes using MoS2-based Schottky photodiodes with asymmetric electrodes integrated with a dual-branch differential circuit. These self-powered devices exhibit a high rectification ratio (>105), fast response time (

Official source

https://infoscience.epfl.ch/entities/publication/71e1b166-f98b-4d84-8cb4-52990eaa3332

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.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2026

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/71e1b166-f98b-4d84-8cb4-52990eaa3332

About this result

Related concepts (32)

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

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.

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.

Related publications (32)

Event-Driven Spike Encoding via Asymmetric Schottky Junction Photodiodes for Bioinspired Vision Systems

Chat with Graph Search

Leveraging neural, muscular, and kinematic signals to enable augmented motor control without interfering with concurrent functions

Inference in spreading processes with neural-network priors

Thinking of Neural Networks like a Physicist: the Statistical Physics of Machine Learning