Relaxation of a Spiking Mott Artificial Neuron

Tesler, Federico; Adda, Coline; Tranchant, Julien; Corraze, B.; Janod, Étienne; Cario, Laurent; Stoliar, Pablo; Rozenberg, M. J.

doi:10.1103/physrevapplied.10.054001

Cited by 18 publications

(22 citation statements)

References 27 publications

(53 reference statements)

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“…The key physical phenomenon in those systems is an unusual thermally driven first-order insulator-metal transition, which may also be induced by a strong electric field 14–16 . An important and attractive feature is that while the Mott materials are challenging to control and fabricate, they may eventually enable the replacement of the whole SCR + RC block of the “soma” with a single two-terminal Mott insulator device 17,18 . This would provide further simplicity and power efficiency for the implementation of the ultimate ultra-compact neuron 2 .…”

Section: Discussionmentioning

confidence: 99%

An ultra-compact leaky-integrate-and-fire model for building spiking neural networks

Rozenberg

Schneegans

Stoliar

2019

Sci Rep

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We introduce an ultra-compact electronic circuit that realizes the leaky-integrate-and-fire model of artificial neurons. Our circuit has only three active devices, two transistors and a silicon controlled rectifier (SCR). We demonstrate the implementation of biologically realistic features, such as spike-frequency adaptation, a refractory period and voltage modulation of spiking rate. All characteristic times can be controlled by the resistive parameters of the circuit. We built the circuit with out-of-the-shelf components and demonstrate that our ultra-compact neuron is a modular block that can be associated to build multi-layer deep neural networks. We also argue that our circuit has low power requirements, as it is normally off except during spike generation. Finally, we discuss the ultimate ultra-compact limit, which may be achieved by further replacing the SCR circuit with Mott materials.

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Section: Discussionmentioning

confidence: 99%

An ultra-compact leaky-integrate-and-fire model for building spiking neural networks

Rozenberg

Schneegans

Stoliar

2019

Sci Rep

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“…The device returns to its original insulating state when the field is removed with a characteristic relaxation time (replicating the refractive period of neurons). That required additional short memory may come from the relaxation time of some volatile resistive switching phenomena [738] , [739] . Pickett et al [740] demonstrated that the Na gain, and the refractory period (Fig 11(a)).…”

Section: Neuristorsmentioning

confidence: 99%

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Pérez‐Tomás

2019

Adv Materials Inter

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New device concepts and new computing principles are needed to balance our ever-growing appetite for data and information with the realization of the goals of increased energy efficiency, reduction in CO 2 emissions and the circular economy. Neuromorphic or synaptic electronics is an emerging field of research aiming to overcome the current computer's Von-Neumann bottleneck by building artificial neuronal systems to mimic the extremely energy efficient biological synapses. The introduction of photovoltaic and/or photonic aspects into these neuromorphic architectures will produce self-powered adaptive electronics but may also open up new possibilities in artificial neuroscience, artificial neural communications, sensing and machine learning which would enable, in turn, a new era for computational systems owing to the possibility of attaining high bandwidths with much reduced power consumption. This perspective is focused on recent progress in the implementation of functional oxide thinfilms into photovoltaic and neuromorphic applications towards the envisioned goal of selfpowered photovoltaic neuromorphic systems or a solar brain.

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“…S9 in ( 31 )]. Each node in the network can be either insulating or metallic, depending on the local temperature through a Landau-type free energy functional that mimics a first-order phase transition ( 11 , 35 ), similar to the IMT present in VO 2 and V 2 O 3 . This functional depends only on the temperature, without any direct contribution from the electric field.…”

mentioning

confidence: 99%

Spatiotemporal characterization of the field-induced insulator-to-metal transition

Valle

Vargas

Rocco

et al. 2021

Science

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Many correlated systems feature an insulator-to-metal transition that can be triggered by an electric field. Although it is known that metallization takes place through filament formation, the details of how this process initiates and evolves remain elusive. We use in-operando optical reflectivity to capture the growth dynamics of the metallic phase with space and time resolution. We demonstrate that filament formation is triggered by nucleation at hotspots, with a subsequent expansion over several decades in time. By comparing three case studies (VO2, V3O5 and V2O3), we identify the resistivity change across the transition as the crucial parameter governing this process. Our results provide a spatiotemporal characterization of volatile resistive switching in Mott insulators, key for emerging technologies such as optoelectronics or neuromorphic computing.

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Relaxation of a Spiking Mott Artificial Neuron

Cited by 18 publications

References 27 publications

An ultra-compact leaky-integrate-and-fire model for building spiking neural networks

An ultra-compact leaky-integrate-and-fire model for building spiking neural networks

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Spatiotemporal characterization of the field-induced insulator-to-metal transition

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