Wave Digital Emulation of a Bio-Inspired Circuit for Axon Growth

Jenderny, Sebastian; Ochs, Karlheinz

doi:10.1109/biocas54905.2022.9948613

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…We would like to stress that the wave digital emulator is not restricted to a single device. In fact, one could use the proposed method to emulate a whole memristive network by using the methods discussed in [19], [30], [32]. This allows the efficient emulation of large memristive networks including RRAM devices with which we hope to support the development of aspiring computation technologies, such as neuromorphic computing or memristive logic.…”

Section: Discussionmentioning

confidence: 99%

“…This statement is supported by the concept of multidimensional wave digital algorithms [16], which allows emulating many electrical components (even with different parameters) in a highly parallel fashion. For example, many wave digital algorithms have been proposed for mermristive circuits on graphs [29], one-port or two-port coupling networks (crossbars) [30], [31], and diffusively coupled oscillators (resistive or memristive) [19], [32]. Lastly, a major advantage of the suggested concept is its real-time capability, which allows implementing the associated algorithms on Application-Specific-Integrated-Circuits (ASICs) or Field-Programmable-Gate-Arrays (FPGAs) and using these devices to replace the actual memristors within the circuit.…”

Section: B An Enhanced Memristor Modelmentioning

confidence: 99%

“…For that reason, we introduce a software wave digital emulator. Wave digital algorithms are known for their massive parallelism, high robustness, and fault tolerance [16]- [18], which allows real-time capable emulation of large networks, even with memristors [19]. More details on this concept are given in section IV.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wave Digital Emulation of an Enhanced Compact Model for RRAM Devices with Multilevel Capability

Beattie¹,

Quesada²,

Uhlmann³

et al. 2022

Preprint

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<p>The reliable and compact modeling of RRAM devices is crucial for supporting the development of novel technologies including them. The latter includes a wide range of applications, such as in-memory computing in neuromorphic networks or memristive logic. A major advantage of the considered HfO2- based RRAM devices is their CMOS-compatibility, which allows them to already be utilized in present applications. However, one problem with RRAMs is that their fabrication still leads to device variabilities. This makes it challenging to test the functionality of aspiring technologies utilizing them in an experimental fashion. This work is dedicated to the compact modeling and efficient emulation of 1T-1R RRAM devices. Specifically, we aim to provide an enhanced model, based on the Stanford-PKU model, that can be used on any simulation platform such as SPICE, VERLIOGA, or even standard ODE solvers to simulate multilevel capable RRAM devices. Furthermore, we provide an algorithmic model, based on the wave digital concept, which allows for emulating the considered RRAM device in real-time. Using the latter, we show the hysteresis of our enhanced model to exhibit astounding resemblance with real device measurements. </p>

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Section: Discussionmentioning

confidence: 99%

Section: B An Enhanced Memristor Modelmentioning

confidence: 99%

See 1 more Smart Citation

Wave Digital Emulation of an Enhanced Compact Model for RRAM Devices with Multilevel Capability

Beattie¹,

Quesada²,

Uhlmann³

et al. 2022

Preprint

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Solving a one-dimensional moving boundary problem based on wave digital principles

Beattie¹,

Ochs²

2023

Multidim Syst Sign Process

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We report on a novel method for solving one-dimensional moving boundary problems based on wave digital principles. Here, we exploit multidimensional wave digital algorithms to derive an efficient and robust algorithm for the solution of the considered problem. Our method lets the wave digital model, on which the algorithm is based, expand according to the size of the solution domain. The expanding model introduces new dynamical elements, which must be properly initialized to obtain a calculable algorithm. To deal with this problem, we make use of linear multistep methods to extrapolate the missing values. Our results show the proposed method to indeed be capable of correctly solving a one-dimensional partial differential equation describing a growing biological axon.

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A memristive circuit for self-organized network topology formation based on guided axon growth

Jenderny,

Ochs,

Xue

2024

Sci Rep

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Circuit implementations of neuronal networks so far have been focusing on synaptic weight changes as network growth principles. Besides these weight changes, however, it is also useful to incorporate additional network growth principles such as guided axon growth and pruning. These allow for dynamical signal delays and a higher degree of self-organization, and can thus lead to novel circuit design principles. In this work we develop an ideal, bio-inspired electrical circuit mimicking growth and pruning controlled by guidance cues. The circuit is based on memristively coupled neuronal oscillators. As coupling element, we use memsensors consisting of a general sensor, two gradient sensors, and two memristors. The oscillators and memsensors are arranged in a grid structure, where oscillators and memsensors realize nodes and edges, respectively. This allows for arbitrary 2D growth scenarios with axon growth controlled by guidance cues. Simulation results show that the circuit successfully mimics a biological example in which two neurons initially grow towards two target neurons, where undesired connections are pruned later on.

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Wave Digital Emulation of a Bio-Inspired Circuit for Axon Growth

Cited by 7 publications

References 13 publications

Wave Digital Emulation of an Enhanced Compact Model for RRAM Devices with Multilevel Capability

Wave Digital Emulation of an Enhanced Compact Model for RRAM Devices with Multilevel Capability

Solving a one-dimensional moving boundary problem based on wave digital principles

A memristive circuit for self-organized network topology formation based on guided axon growth

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