Mr. Xiangzun Wang Profile

Xiangzun Wang

at Univ. Leipzig

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 October 2022 Presentation

Realization of reservoir computing by delayed oscillators with active microparticles (Conference Presentation)

Xiangzun Wang, Frank Cichos

Proceedings Volume PC12204, PC1220417 (2022) https://doi.org/10.1117/12.2633310

KEYWORDS: Oscillators, Particles, Liquids, Neural networks, Multiplexing, Electronic circuits, Computer architecture

Read Abstract +

Reservoir computing as a highly efficient architecture for recurrent neural networks has been implemented in a variety of ways, including anharmonic oscillators, liquid surfaces, and optical and electronic circuits. Here, we investigate whether active particle networks that mimic fundamental dynamical processes of living systems can serve as reservoirs. In particular, we realize active particle oscillators, each consisting of an immobile and an active colloidal microparticle suspended in a layer of a liquid solution. The motion of the active particles is manipulated by a feedback system using a focused laser that stimulates the particles to float in 2D by thermophoresis [1]. The active particle is programmed to be attracted to the immobile particle with a delayed response that exhibits a pitchfork bifurcation, which introduces nonlinearity and memory into the response of a single active oscillator. Using time multiplexing, the propulsion of the oscillator is selected at different times as virtual nodes of a reservoir that are coupled to an input layer. Since the motion of the active particle is affected in a nonlinear manner with a memory of its previous state, the last node state is naturally coupled to the other nodes from different iterations due to the intrinsic property of the delayed oscillator. We illustrate the performance of the reservoir consisting of multiple oscillators with different delays by the tasks of nonlinear prediction and classification. [1]. F. Martin et al. ACS Nano 15, 2, 3434-3440 (2021)

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years