Theoretical simulations of dynamical systems for advanced reservoir computing applications
Doctoral thesis, 2020

There are computational problems that are simply too complex and cannot be handled by traditional CMOS technologies due to practical engineering limitations related to either fundamental physical behavior of devices at small scales, or various energy consumption issues. The field of unconventional computation has emerged as a response to these challenges. Up to date unconventional computation encompasses a plethora of computing frameworks, such as neuromorphic computing, molecular computing, reaction-diffusion computing, or quantum computing, and is ever increasing in its scope. This thesis is biased towards developing sensing applications in the unconventional computing context. This initiative is further extended towards developing novel machine learning applications.

The possibility of building intelligent dynamical systems that collect information and analyze it in real-time has been investigated theoretically.
The basic idea is to expose a dynamical system to the environment one wishes to analyze over time. The system operates as an environment sensitive reservoir computer. Since the state of the reservoir depends on the environment, the information about the environment one wishes to retrieve gets encoded in the state of the system. The key idea exploited in the thesis is that if the state of the reservoir is highly correlated with the state of the environment  then the information about the environment can be inferred with a modest engineering overhead.

A typical dynamical system is assumed to be a network of environment sensitive elements. Each element can be something simple, but taken together, the elements acquire collective intelligence that can be harvested. These ideas have been examined theoretically (and verified experimentally) by simulating various networks of environment-sensitive elements: the memristor, the capacitor, the constant phase element and the organic field effect transistor element. The simulations were done in the context of ion sensing, which is an extremely complex, many-body, and multi-scale modeling problem.

Kollektorn, MC2, Chalmers
Opponent: Andrew Adamatzky, University of the West of England, FET- computer science and creative technologies, Bristol, United Kingdom

Author

Vasileios Athanasiou

Chalmers, Microtechnology and Nanoscience (MC2), Electronics Material and Systems Laboratory

On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks

International Journal of Parallel, Emergent and Distributed Systems,; Vol. 33(2018)p. 367-386

Journal article

Athanasiou V., Konkoli Z., On the use of collaborative interactions for embedded sensing applications: Memristor networks as intelligent sensing substrates

On the efficient simulation of electrical circuits with constant phase elements: The Warburg element as a test case

International Journal of Circuit Theory and Applications,; Vol. 46(2018)p. 1072-1090

Journal article

On a generic theory of the organic electrochemical transistor dynamics

Organic Electronics: physics, materials, applications,; Vol. 72(2019)p. 39-49

Journal article

Memristor Models for Early Detection of Sepsis in ICU patients

2019 Computing in Cardiology,; Vol. 46(2020)

Paper in proceedings

On sensing principles using temporally extended bar-codes

IEEE Sensors Journal,; Vol. 20(2020)p. 6782-6791

Journal article

On Mathematics of Universal Computation with Generic Dynamical Systems

From Parallel to Emergent Computing,; (2019)p. 385-405

Book chapter

Athanasiou V., Konkoli Z., On Improving the Computing Capacity of Dynamical Systems

Reservoir Computing with Real-time Data for future IT (RECORD-IT)

European Commission (Horizon 2020), 2015-09-01 -- 2018-08-31.

Areas of Advance

Information and Communication Technology

Subject Categories

Computational Mathematics

Other Physics Topics

Computer Science

ISBN

978-91-7905-293-5

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4760

Publisher

Chalmers University of Technology

Kollektorn, MC2, Chalmers

Opponent: Andrew Adamatzky, University of the West of England, FET- computer science and creative technologies, Bristol, United Kingdom

More information

Latest update

5/19/2020