Visualizing a joint future of neuroscience and neuromorphic engineering

Zenke, Friedemann; Bohté, Sander M.; Clopath, Claudia; Comşa, Iulia-Maria; Göltz, Julian; Maass, Wolfgang; Masquelier, Timothée; Naud, Richard; Neftci, Emre; Petrovici, Mihai A.; Scherr, Franz; Goodman, Dan F. M.

doi:10.1016/j.neuron.2021.01.009

Cited by 55 publications

(46 citation statements)

References 9 publications

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“…Currently, further increases in the efficiency of big-data processing are limited by the resolution and the cost of manufacturing complementary metal oxide semiconductor (CMOS) transistors less than 10 nanometers in size, creating the difficulty of sustainable and cost-effective scaling [ 7 , 8 ]. Therefore, there is a need to develop and research a new base of non-volatile elements of electronics with increased computational efficiency, which would meet the requirements of the information-technology market [ 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures

et al. 2022

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This article presents the results of experimental studies of the impact of electrode material and the effect of nanoscale film thickness on the resistive switching in forming-free nanocrystalline ZnO films grown by pulsed laser deposition. It was demonstrated that the nanocrystalline ZnO film with TiN, Pt, ZnO:In, and ZnO:Pd bottom electrodes exhibits a nonlinear bipolar effect of forming-free resistive switching. The sample with Pt showed the highest resistance values RHRS and RLRS and the highest value of Uset = 2.7 ± 0.4 V. The samples with the ZnO:In and ZnO:Pd bottom electrode showed the lowest Uset and Ures values. An increase in the number of laser pulses from 1000 to 5000 was shown to lead to an increase in the thickness of the nanocrystalline ZnO film from 7.2 ± 2.5 nm to 53.6 ± 18.3 nm. The dependence of electrophysical parameters (electron concentration, electron mobility, and resistivity) on the thickness of the forming-free nanocrystalline ZnO film for the TiN/ZnO/W structure was investigated. The endurance test and homogeneity test for TiN/ZnO/W structures were performed. The structure Al2O3/TiN/ZnO/W with a nanocrystalline ZnO thickness 41.2 ± 9.7 nm was shown to be preferable for the manufacture of ReRAM and memristive neuromorphic systems due to the highest value of RHRS/RLRS = 2307.8 ± 166.4 and low values of Uset = 1.9 ± 0.2 V and Ures = −1.3 ± 0.5 V. It was demonstrated that the use of the TiN top electrode in the Al2O3/TiN/ZnO memristor structure allowed for the reduction in Uset and Ures and the increase in the RHRS/RLRS ratio. The results obtained can be used in the manufacturing of resistive-switching nanoscale devices for neuromorphic computing based on the forming-free nanocrystalline ZnO oxide films.

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Section: Introductionmentioning

confidence: 99%

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures

et al. 2022

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“…[ 4 , 5 ] Neuromorphic approaches rely on spike‐based encoding of information and bio‐inspired learning rules for computing time dependent signals generated from different sensory modalities. [ 6 ] There are already great successes in this field with the recent neuromorphic chips [ 7 ] that have demonstrated record energy consumption, but there still exist several challenges that require the development of new solutions. [ 3 , 5 , 8 ] Firstly, biology relies on a large variety of information carriers.…”

Section: Introductionmentioning

confidence: 99%

Dendritic Organic Electrochemical Transistors Grown by Electropolymerization for 3D Neuromorphic Engineering

et al. 2021

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One of the major limitations of standard top‐down technologies used in today's neuromorphic engineering is their inability to map the 3D nature of biological brains. Here, it is shown how bipolar electropolymerization can be used to engineer 3D networks of PEDOT:PSS dendritic fibers. By controlling the growth conditions of the electropolymerized material, it is investigated how dendritic fibers can reproduce structural plasticity by creating structures of controllable shape. Gradual topologies evolution is demonstrated in a multielectrode configuration. A detailed electrical characterization of the PEDOT:PSS dendrites is conducted through DC and impedance spectroscopy measurements and it is shown how organic electrochemical transistors (OECT) can be realized with these structures. These measurements reveal that quasi‐static and transient response of OECTs can be adjusted by controlling dendrites’ morphologies. The unique properties of organic dendrites are used to demonstrate short‐term, long‐term, and structural plasticity, which are essential features required for future neuromorphic hardware development.

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“…It focuses on biological plausibility to validate its models and in comparison with experimental data. As a result, both fields have a common interest in neural information processing and how these may implement the computations 870 that happen in the brain (for a review, see [47]).…”

Section: Discussionmentioning

confidence: 99%

A robust event-driven approach to always-on object recognition

Grimaldi¹,

Boutin²,

Ieng³

et al. 2022

Preprint

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<div> <div> <div> <p>We propose a neuromimetic architecture able to perform always-on pattern recognition. To achieve this, we extended an existing event-based algorithm [1], which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events acquired by a neuromorphic camera, these time surfaces allow to code the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we extended this method to increase its performance. Our first contribution was to add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns [2]. A second contribution is to draw an analogy between the HOTS algorithm and Spiking Neural Networks (SNN). Following that analogy, our last contribution is to modify the classification layer and remodel the offline pattern categorization method previously used into an online and event-driven one. This classifier uses the spiking output of the network to define novel time surfaces and we then perform online classification with a neuromimetic implementation of a multinomial logistic regression. Not only do these improvements increase consistently the performances of the network, they also make this event-driven pattern recognition algorithm online and bio-realistic. Results were validated on different datasets: DVS barrel [3], Poker-DVS [4] and N-MNIST [5]. We foresee to develop the SNN version of the method and to extend this fully event-driven approach to more naturalistic tasks, notably for always-on, ultra-fast object categorization. </p> </div> </div> </div>

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Visualizing a joint future of neuroscience and neuromorphic engineering

Cited by 55 publications

References 9 publications

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures

Dendritic Organic Electrochemical Transistors Grown by Electropolymerization for 3D Neuromorphic Engineering

A robust event-driven approach to always-on object recognition

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