ArticleFrontiers in neuroscience2015
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.
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Who cites it
111 citing papers in PubMed.
- Corticomorphic Hybrid CNN-SNN Architecture for EEG-Based Low-Footprint Low-Latency Auditory Attention Detection.Annals of biomedical engineering · 2026Article
- Array of Cointegrated Transistor-Based Artificial Neurons and Synapses for Neuromorphic Computing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications.Chemical reviews · 2026Review
- Characterization of a Spiking Convolutional Processor for FPGA.Sensors (Basel, Switzerland) · 2026Article
- Feedback neurons based on perovskite memristor with nickel single-atom engineered reduced graphene oxide cathode.Nature communications · 2026Article
- NeoHebbian synapses to accelerate online training of neuromorphic hardware.Scientific reports · 2026Article
- Advancing neuroengineering with Neuromorphic Twins.Nature communications · 2026Review
- Improving performance in SNN-based deep reinforcement learning via transition-aware embeddings.Frontiers in neurorobotics · 2026Article
- Biological fidelity: The engine driving the neuromorphic renaissance.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Biohybrid Computing with Proteinoids and Algae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A neuromorphic processor with on-chip learning for beyond-CMOS device integration.Nature communications · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Scalable Microscale Artificial Synapses of Lead Halide Perovskite with Femtojoule Energy Consumption.ACS energy letters · 2024Article
- The backpropagation algorithm implemented on spiking neuromorphic hardware.Nature communications · 2024Article
- BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network.Nature communications · 2024Article
- Neuron Circuit Based on a Split-gate Transistor with Nonvolatile Memory for Homeostatic Functions of Biological Neurons.Biomimetics (Basel, Switzerland) · 2024Article
- A lightweight data-driven spiking neuronal network model ofFrontiers in neuroscience · 2024Article
- Direct training high-performance deep spiking neural networks: a review of theories and methods.Frontiers in neuroscience · 2024Review
51 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.
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