ArticleNature communications2024
Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Reconfigurable Memristive Spiking Neuron Enabling Advanced Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial neural manifolds.Nature communications · 2026Article
- Low-Latency Visuotactile Neuron Using Self-Oscillating Memristor.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- High encoding-sensitivity vision sensor with complementary nonlinear neuromorphic computing.Nature communications · 2026Article
- Advances in neuroprostheses: interfaces, materials, and applications.Nano convergence · 2026Review
- An On-Demand Neuromorphic Vision System Enabled by a Multi-Paradigm Neuromorphic Device and Hierarchical Reconfigurability Designed from Device to System Level.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation.Nature communications · 2026Article
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Signal processing and resistive switching mechanisms of niobium oxides.Science and technology of advanced materials · 2026Review
- Frequency Switching Neuristor for Realizing Intrinsic Plasticity and Enabling Robust Neuromorphic Computing.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Memristor-Based Spiking Neuromorphic Systems Toward Brain-Inspired Perception and Computing.Nanomaterials (Basel, Switzerland) · 2025Review
- Bio-plausible reconfigurable spiking neuron for neuromorphic computing.Science advances · 2025Article
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Authors and funding
17 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.
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