Melika Payvand

Assistant Professor

Switzerland

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Melika Payvand is an Assistant Professor at the Institute of Neuroinformatics in Switzerland. Her research focuses on neuromorphic computing, spiking neural networks, and innovative hardware architectures. Recent articles by Dr. Payvand include work on event-based gradients for training neuromorphic systems and the application of 3D technologies in scaling these systems.

Articles (16)

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2025

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2022

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2022

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2022

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2021

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2020

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2020

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2020

Collaborators (4)

Giacomo Indiveri

SWITZERLAND

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Wei Lu

Professor

University of Michigan

UNITED STATES

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Peng Zeng

ETH Zürich

SWITZERLAND

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Bernabé Linares-Barranco

SPAIN

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Melika Payvand

Research Interests

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Articles (16)

DelGrad: exact event-based gradients for training delays and weights on spiking neuromorphic hardware

Author Correction: DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

Scaling Limits of Memristor-Based Routers for Asynchronous Neuromorphic Systems

Editorial: Focus issue on in-memory computing

Scaling neuromorphic systems with 3D technologies

Spike frequency adaptation: bridging neural models and neuromorphic applications

Flexible Unipolar IGZO Transistor-Based Integrate and Fire Neurons for Spiking Neuromorphic Applications

Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing

Author Correction: Self-organization of an inhomogeneous memristive hardware for sequence learning

Self-organization of an inhomogeneous memristive hardware for sequence learning

Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

On-Chip Error-Triggered Learning of Multi-Layer Memristive Spiking Neural Networks

Low-Energy and Fast Spiking Neural Network For Context-Dependent Learning on FPGA

Collaborators (4)

Giacomo Indiveri

Wei Lu

Peng Zeng

Bernabé Linares-Barranco

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