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Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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Updated: May 12, 2026

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

生物模拟视觉系统由二维等离子光传感器阵列实现,用于处理时空信息.

Tian Zhang¹, Linjun Li^1,2

¹State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China.

Small (Weinheim an der Bergstrasse, Germany)

|June 16, 2025

概括

此摘要是机器生成的。

研究人员开发了一种新型的传感器内尖端神经网络 (SNN),模仿人类视觉以实现高效的时空处理. 该设备可实现高精度,零能耗,用于视觉数据感知.

关键词:

2D等离子体光传感器阵列.不同结构异构结构.热电子是一种热的电子.在-传感器尖端神经网络.尖列车是指尖列车.时间编码时间编码.没有偏差的光电流.

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Dual Raster-Scanning Photoacoustic Small-Animal Imager for Vascular Visualization

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Published on: July 15, 2020

相关实验视频

Last Updated: May 12, 2026

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

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Published on: June 23, 2018

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科学领域:

神经形态工程的神经形态工程
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

人类视觉系统在低功耗,连续的时空感知方面表现出色,与当前的机器视觉不同.
现有的神经形态设备缺乏生物忠实性,专注于空间图像识别或有限的事件驱动信息.
在能够高效处理空间和时间视觉数据的设备中存在一个差距,这种设备具有生物现实主义.

研究的目的:

介绍一个在传感器中增强的神经网络 (SNN),复制人类视觉系统的能力.
开发一种能够高效,低功耗感知,预处理,编码和处理驱动的时空信息的设备.
克服当前神经形态系统在处理复杂视觉数据方面的局限性.

主要方法:

开发了一个与SNN集成的2D等离子体光传感器阵列 (PPSA).
利用等离子热电子的光热电效应来实现时空数据的零能量感知.
将光学信号编码到尖列车中,使用具有500 ns采样周期的随机电信号.

主要成果:

该PPSA-SNN系统实现了96%的识别准确度.
证明了对具有混乱时间顺序的模式的准确解释.
在感知全像素时空数据时实现零能耗.

结论:

展示的传感器内SNN有效地模仿了人类视觉系统的时空处理.
该设备为先进的视觉数据分析提供了一个生物学上可信和节能的解决方案.
这项技术对下一代神经形态计算和人工视觉系统具有前景.