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# Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications

Yoeri van de Burgt, Francesca Santoro, Benjamin Tee, Fabien Alibart

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## To cite this version:

Yoeri van de Burgt, Francesca Santoro, Benjamin Tee, Fabien Alibart. Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications. Neuromorphic Computing and Engineering, 2023, 3 (4), pp.040202. 10.1088/2634-4386/ad06ca. hal-04348640

HAL Id: hal-04348640

https://hal.science/hal-04348640

Submitted on 16 May 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

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Distributed under a Creative Commons Attribution 4.0 International License
# Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications

Yoeri van de Burgt, Francesca Santoro, Benjamin Tee, Fabien Alibart

<figure><img src="image_1.png" /></figure>

## To cite this version:

Yoeri van de Burgt, Francesca Santoro, Benjamin Tee, Fabien Alibart. Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications. Neuromorphic Computing and Engineering, 2023, 3 (4), pp.040202. 10.1088/2634-4386/ad06ca. hal-04348640

HAL Id: hal-04348640

https://hal.science/hal-04348640v1

Submitted on 16 May 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
EDITORIAL • OPEN ACCESS

Editorial: Focus on organic materials, bio-
interfacing and processing in neuromorphic
computing and artificial sensory applications

To cite this article: Yoeri van de Burgt et al 2023 *Neuromorph. Comput. Eng.* 3 040202

View the [article online](#) for updates and enhancements.

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EDITORIAL • OPEN ACCESS

Editorial: Focus on organic materials, bio-
interfacing and processing in neuromorphic
computing and artificial sensory applications

To cite this article: Yoeri van de Burgt et al 2023 Neuromorph. Comput. Eng. 3 040202

View the [article online](#) for updates and enhancements.

You may also like

- Defining Millisecond Pulsars

Priyam Halder, Satyaki Goswami,
Protyusha Halder et al.

- The Distribution of High-excitation OCS Emission Around the Cep A-East Protostellar Cluster

C. Codella, R. Bachiller and S. Viti

- Identifying SuperWASP Detected Candidate Variables with TESS

Ai-Ying Zhou
<figure><img src="image_4.png" /></figure>

CrossMark

OPEN ACCESS

RECEIVED
9 October 2023

ACCEPTED FOR PUBLICATION
25 October 2023

PUBLISHED
7 November 2023

Original content from
this work may be used
under the terms of the
Creative Commons
Attribution 4.0 licence.

Any further distribution
of this work must
maintain attribution to
the author(s) and the title
of the work, journal
citation and DOI.

<figure><img src="image_5.png" /></figure>

EDITORIAL

# Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications

Yoeri van de Burgt<fnref n="1" />,<fnref n="2" /> ID, Francesca Santoro<fnref n="3" />,<fnref n="4" />, Benjamin Tee<fnref n="5" />,<fnref n="6" /> and Fabien Alibart<fnref n="7" />,<fnref n="8" />,<fnref n="9" />

Artificial intelligence (AI) and deep learning rely on artificial neural networks that are typically executed on conventional von Neumann architecture-based computers, mostly operating in a sequential manner. In contrast, the human nervous system is a massively distributed, parallel, and interconnected system, with superior functionality and performance in data processing and adaptive learning compared to digital computers. Neuromorphic computing and engineering aims to replicate some of the functionalities and architectures of the human brain and nervous system with dedicated materials and devices. Realizing computational primitives of biological neural networks at the device and circuit level is the first step towards efficient neuromorphic computing systems that are able to sense, perceive, analyze, interpret and act upon a dynamic, real-world environment. Such neuromorphic approaches cover aspects associated to computing of complex time-dependent signals as well as signal encoding from sensory units in a bioinspired way.

Organic electronic materials present an attractive option for such systems, offering the potential for biocompatible and relatively cost-effective neuromorphic devices with low-energy switching capabilities and excellent tunability. Tunable organic electronic materials and devices may serve as attractive alternatives to traditional memristive devices, particularly in specific neuromorphic applications like online learning, where synaptic weights are trained on-chip for real-time predictions. The distinctive properties of organic materials may introduce novel and alternative switching mechanisms that exhibit reduced stochasticity while still maintaining low energy consumption and a wide dynamic range, ultimately leading to improved training and inference accuracy. These distinctives features of organic electronics could also be a unique opportunity to provide neuromorphic engineers with new hardware implementations of bio-inspired mechanisms. At the same time organic materials operate with ions and can be biocompatible, which opens up opportunities at the interface with biology. In this special issue we focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications.

Despite recent advantages for organic neuromorphic systems for in memory computing, one of the main challenges remains the stability and in particular the state retention. In our special issue Felder et al [1] introduce a method to overcome this issue by 'reminding' the neuromorphic devices at certain times. This way the classification accuracy can remain high with a simple action.

Using a similar network of organic ECRAMs, which is traditionally implemented for in-memory computing, the same authors show in Felder et al [2] that it is also possible to run spiking neural networks. They demonstrate that their non-ideal devices that 'forget' their state, are actually 'reminded' by the spikes, and thus keep the high classification accuracy.

Mimicking spiking behavior is an important step towards artificial systems operating at the interface with biology. In Hosseine et al [3] the authors show a biologically realistic spiking synaptic circuit on a biocompatible flexible substrate.

On the other hand, for temporal systems such as many biological signals like ECG it can also be beneficial to use reservoir computing. This type of architecture uses a non-linear transformation of input signals to allow them to be linearly separated. In software this concept is well understood but in hardware many

<footnote n="1">¹ Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands</footnote>

<footnote n="2">² Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands</footnote>

<footnote n="3">³ Faculty of Electrical Engineering and IT, RWTH Aachen, Aachen, Germany</footnote>

<footnote n="4">⁴ Institute of Biological Information Processing—Bioelectronics, Forschungszentrum, Juelich, Germany</footnote>

<footnote n="5">⁵ Department of Materials Science and Engineering (MSE), National University of Singapore, Singapore, Singapore</footnote>

<footnote n="6">⁶ Department of Electrical and Computer Engineering (ECE), National University of Singapore, Singapore, Singapore</footnote>

<footnote n="7">⁷ Institut Interdisciplinaire d'Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, Canada</footnote>

<footnote n="8">⁸ Laboratoire Nanotechnologies Nanosystèmes (LN2)—CNRS UMI-3463, Université de Sherbrooke, Sherbrooke, Canada</footnote>

<footnote n="9">⁹ Institute of Electronics, Microelectronics and Nanotechnology (IEMN), Université de Lille, Villeneuve d'Ascq, France</footnote>
<figure><img src="image_3.png" /></figure>

CrossMark

OPEN ACCESS

RECEIVED
9 October 2023

ACCEPTED FOR PUBLICATION
25 October 2023

PUBLISHED
7 November 2023

Original content from
this work may be used
under the terms of the
Creative Commons
Attribution 4.0 licence.

Any further distribution
of this work must
maintain attribution to
the author(s) and the title
of the work, journal
citation and DOI.

<figure><img src="image_4.png" /></figure>

EDITORIAL

# Editorial: Focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications

Yoeri van de Burgt<fnref n="1" />,<fnref n="2" /> ID, Francesca Santoro<fnref n="3" />,<fnref n="4" />, Benjamin Tee<fnref n="5" />,<fnref n="6" /> and Fabien Alibart<fnref n="7" />,<fnref n="8" />,<fnref n="9" />

Artificial intelligence (AI) and deep learning rely on artificial neural networks that are typically executed on conventional von Neumann architecture-based computers, mostly operating in a sequential manner. In contrast, the human nervous system is a massively distributed, parallel, and interconnected system, with superior functionality and performance in data processing and adaptive learning compared to digital computers. Neuromorphic computing and engineering aims to replicate some of the functionalities and architectures of the human brain and nervous system with dedicated materials and devices. Realizing computational primitives of biological neural networks at the device and circuit level is the first step towards efficient neuromorphic computing systems that are able to sense, perceive, analyze, interpret and act upon a dynamic, real-world environment. Such neuromorphic approaches cover aspects associated to computing of complex time-dependent signals as well as signal encoding from sensory units in a bioinspired way.

Organic electronic materials present an attractive option for such systems, offering the potential for biocompatible and relatively cost-effective neuromorphic devices with low-energy switching capabilities and excellent tunability. Tunable organic electronic materials and devices may serve as attractive alternatives to traditional memristive devices, particularly in specific neuromorphic applications like online learning, where synaptic weights are trained on-chip for real-time predictions. The distinctive properties of organic materials may introduce novel and alternative switching mechanisms that exhibit reduced stochasticity while still maintaining low energy consumption and a wide dynamic range, ultimately leading to improved training and inference accuracy. These distinctives features of organic electronics could also be a unique opportunity to provide neuromorphic engineers with new hardware implementations of bio-inspired mechanisms. At the same time organic materials operate with ions and can be biocompatible, which opens up opportunities at the interface with biology. In this special issue we focus on organic materials, bio-interfacing and processing in neuromorphic computing and artificial sensory applications.

Despite recent advantages for organic neuromorphic systems for in memory computing, one of the main challenges remains the stability and in particular the state retention. In our special issue Felder *et al* [1] introduce a method to overcome this issue by 'reminding' the neuromorphic devices at certain times. This way the classification accuracy can remain high with a simple action.

Using a similar network of organic ECRAMs, which is traditionally implemented for in-memory computing, the same authors show in Felder *et al* [2] that it is also possible to run spiking neural networks. They demonstrate that their non-ideal devices that 'forget' their state, are actually 'reminded' by the spikes, and thus keep the high classification accuracy.

Mimicking spiking behavior is an important step towards artificial systems operating at the interface with biology. In Hosseine *et al* [3] the authors show a biologically realistic spiking synaptic circuit on a biocompatible flexible substrate.

On the other hand, for temporal systems such as many biological signals like ECG it can also be beneficial to use reservoir computing. This type of architecture uses a non-linear transformation of input signals to allow them to be linearly separated. In software this concept is well understood but in hardware many

<footnote n="1">¹ Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands</footnote>

<footnote n="2">² Microsystems, Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands</footnote>

<footnote n="3">³ Faculty of Electrical Engineering and IT, RWTH Aachen, Aachen, Germany</footnote>

<footnote n="4">⁴ Institute of Biological Information Processing—Bioelectronics, Forschungszentrum, Juelich, Germany</footnote>

<footnote n="5">⁵ Department of Materials Science and Engineering (MSE), National University of Singapore, Singapore, Singapore</footnote>

<footnote n="6">⁶ Department of Electrical and Computer Engineering (ECE), National University of Singapore, Singapore, Singapore</footnote>

<footnote n="7">⁷ Institut Interdisciplinaire d'Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, Canada</footnote>

<footnote n="8">⁸ Laboratoire Nanotechnologies Nanosystèmes (LN2)—CNRS UMI-3463, Université de Sherbrooke, Sherbrooke, Canada</footnote>

<footnote n="9">⁹ Institute of Electronics, Microelectronics and Nanotechnology (IEMN), Université de Lille, Villeneuve d'Ascq, France</footnote>
practical issues arise. Fortunately, in Cucchi et al [4] the authors present a very practical hands-on tutorial to start implementing reservoir computing in hardware.

Another alternative approach to synaptic crossbar arrays of ECRAM devices, is to grow conductive dendritic polymeric fibers *in situ* to reproduce the interconnections in between nodes in a neural network. Scholaert et al [5] show that such dendritic objects have non-linear current-voltage responses that could be advantageously used to classify spiking signals, such as spiking activity that would be produced by biological neural cells.

However, true organic neuromorphic systems operating at the biointerface are not yet available and more research is certainly needed to achieve this ambitious goal. In Bruno et al [6] the authors give a perspective of how some of the challenges towards this goal can be overcome including organic material design to interface cell, tissues and organs and potentially bridge to more sophisticated inorganic computing units for closed-loop control sensing and actuation.

Finally, in Field et al [7] a tantalizing concept is introduced that states that any system with morphological degrees of freedom and locally limited free energy will eventually evolve toward a neuromorphic morphology that supports hierarchical computations. These hierarchies occur throughout biology, from the architectures of intracellular signal-transduction pathways to the large-scale organization of perception and action processing in the mammalian brain.

**Data availability statement**

No new data were created or analysed in this study.

**ORCID iD**

Yoeri van de Burgt https://orcid.org/0000-0003-3472-0148

**References**

[1] Felder D, Muche K, Linkhorst J and Wessling M 2022 Reminding forgetful organic neuromorphic device networks *Neuromorph. Comput. Eng.* 2 044014

[2] Felder D, Linkhorst J and Wessling M 2023 Spiking neural networks compensate weight drift in organic neuromorphic device networks *Neuromorph. Comput. Eng.* 3 024008

[3] Hosseini M J M, Yang Y, Prendergast A J, Donati E, Faezipour M, Indiveri G and Nawrocki R A 2022 An organic synaptic circuit: toward flexible and biocompatible organic neuromorphic processing *Neuromorph. Comput. Eng.* 2 034009

[4] Cucchi M, Abreu S, Ciccone G, Brunner D and Kleemann H 2022 Hands-on reservoir computing: a tutorial for practical implementation *Neuromorph. Comput. Eng.* 2 032002

[5] Scholaert C, Janzakova K, Coffinier Y, Alibart F and Pecqueur S 2022 Plasticity of conducting polymer dendrites to bursts of voltage spikes in phosphate buffered saline *Neuromorph. Comput. Eng.* 2 044010

[6] Bruno U, Mariano A, Rana D, Gemmeke T, Musall S and Santoro F 2023 From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks *Neuromorph. Comput. Eng.* 3 023002

[7] Fields C, Friston K, Glazebrook J F, Levin M and Marcianò A 2022 The free energy principle induces neuromorphic development *Neuromorph. Comput. Eng.* 2 042002
practical issues arise. Fortunately, in Cucchi et al [4] the authors present a very practical hands-on tutorial to start implementing reservoir computing in hardware.

Another alternative approach to synaptic crossbar arrays of ECRAM devices, is to grow conductive dendritic polymeric fibers *in situ* to reproduce the interconnections in between nodes in a neural network. Scholaert et al [5] show that such dendritic objects have non-linear current-voltage responses that could be advantageously used to classify spiking signals, such as spiking activity that would be produced by biological neural cells.

However, true organic neuromorphic systems operating at the biointerface are not yet available and more research is certainly needed to achieve this ambitious goal. In Bruno et al [6] the authors give a perspective of how some of the challenges towards this goal can be overcome including organic material design to interface cell, tissues and organs and potentially bridge to more sophisticated inorganic computing units for closed-loop control sensing and actuation.

Finally, in Field et al [7] a tantalizing concept is introduced that states that any system with morphological degrees of freedom and locally limited free energy will eventually evolve toward a neuromorphic morphology that supports hierarchical computations. These hierarchies occur throughout biology, from the architectures of intracellular signal-transduction pathways to the large-scale organization of perception and action processing in the mammalian brain.

## Data availability statement

No new data were created or analysed in this study.

## ORCID iD

Yoeri van de Burgt https://orcid.org/0000-0003-3472-0148

## References

[1] Felder D, Muche K, Linkhorst J and Wessling M 2022 Reminding forgetful organic neuromorphic device networks *Neuromorph. Comput. Eng.* 2 044014

[2] Felder D, Linkhorst J and Wessling M 2023 Spiking neural networks compensate weight drift in organic neuromorphic device networks *Neuromorph. Comput. Eng.* 3 024008

[3] Hosseini M J M, Yang Y, Prendergast A J, Donati E, Faezipour M, Indiveri G and Nawrocki R A 2022 An organic synaptic circuit: toward flexible and biocompatible organic neuromorphic processing *Neuromorph. Comput. Eng.* 2 034009

[4] Cucchi M, Abreu S, Ciccone G, Brunner D and Kleemann H 2022 Hands-on reservoir computing: a tutorial for practical implementation *Neuromorph. Comput. Eng.* 2 032002

[5] Scholaert C, Janzakova K, Coffinier Y, Alibart F and Pecqueur S 2022 Plasticity of conducting polymer dendrites to bursts of voltage spikes in phosphate buffered saline *Neuromorph. Comput. Eng.* 2 044010

[6] Bruno U, Mariano A, Rana D, Gemmeke T, Musall S and Santoro F 2023 From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks *Neuromorph. Comput. Eng.* 3 023002

[7] Fields C, Friston K, Glazebrook J F, Levin M and Marcianò A 2022 The free energy principle induces neuromorphic development *Neuromorph. Comput. Eng.* 2 042002