ArticleProceedings of the National Academy of Sciences of the United States of America2011
A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 207 citations in OpenAlex.
- A Perovskite Memdiode-Based Neuromorphic in Silico Surrogate Model for Emulating Predictive Coding Failure in Diabetic Neuropathy.Neuroinformatics · 2026Article
- Self-Powered Neuromorphic Systems Based on Tribotronics Synaptic Devices.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Modeling homosynaptic and heterosynaptic plasticity with a single neuromemristive synapse.Journal of advanced research · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Advancing Flexible Optoelectronic Synapses and Neurons with MXene-Integrated Polymeric Platforms.Nanomaterials (Basel, Switzerland) · 2025Review
- Volatile tin oxide memristor for neuromorphic computing.iScience · 2024Article
- Hybrid CMOS-Memristor synapse circuits for implementing Ca ion-based plasticity model.Scientific reports · 2024Article
- Photoferroelectric All-van-der-Waals Heterostructure for Multimode Neuromorphic Ferroelectric Transistors.ACS applied materials & interfaces · 2023Article
- Hardware Demonstration of SRDP Neuromorphic Computing with Online Unsupervised Learning Based on Memristor Synapses.Micromachines · 2022Article
- Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing.Nano-micro letters · 2022Review
- Implementation of Neuro-Memristive Synapse for Long-and Short-Term Bio-Synaptic Plasticity.Sensors (Basel, Switzerland) · 2021Article
- Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs.Frontiers in neuroscience · 2021Article
- One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications.Science advances · 2020Article
- Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices.Nature communications · 2020Article
- Memristive and CMOS Devices for Neuromorphic Computing.Materials (Basel, Switzerland) · 2020Review
- Memristor Neural Network Training with Clock Synchronous Neuromorphic System.Micromachines · 2019Article
- Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models.Frontiers in computational neuroscience · 2018Review
- Dendritic and Axonal Propagation Delays May Shape Neuronal Networks With Plastic Synapses.Frontiers in physiology · 2018Review
- Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film.Scientific reports · 2016Article
- Pulse Shape and Timing Dependence on the Spike-Timing Dependent Plasticity Response of Ion-Conducting Memristors as Synapses.Frontiers in bioengineering and biotechnology · 2016Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.