ArticleScientific reports2015
Reliability of neuronal information conveyed by unreliable neuristor-based leaky integrate-and-fire neurons: a model study.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
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- Memristor-Based Spiking Neuromorphic Systems Toward Brain-Inspired Perception and Computing.Nanomaterials (Basel, Switzerland) · 2025Review
- Efficient implementation of the Hodgkin-Huxley potassium channel via a single volatile memristor.Frontiers in neuroscience · 2025Article
- Review on spiking neural network-based ECG classification methods for low-power environments.Biomedical engineering letters · 2024Review
- A VOMicromachines · 2023Article
- Biological function simulation in neuromorphic devices: from synapse and neuron to behavior.Science and technology of advanced materials · 2023Review
- Bifurcation and Entropy Analysis of a Chaotic Spike Oscillator Circuit Based on the S-Switch.Entropy (Basel, Switzerland) · 2022Article
- Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing.Nature communications · 2022Article
- Emerging Artificial Neuron Devices for Probabilistic Computing.Frontiers in neuroscience · 2021Review
- Biological plausibility and stochasticity in scalable VONature communications · 2018Article
- Neural-like computing with populations of superparamagnetic basis functions.Nature communications · 2018Article
- Leaky Integrate-and-Fire Neuron Circuit Based on Floating-Gate Integrator.Frontiers in neuroscience · 2016Article
- A memristive spiking neuron with firing rate coding.Frontiers in neuroscience · 2015Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We conducted simulations on the neuronal behavior of neuristor-based leaky integrate-and-fire (NLIF) neurons. The phase-plane analysis on the NLIF neuron highlights its spiking dynamics--determined by two nullclines conditional on the variables on the plane. Particular emphasis was placed on the operational noise arising from the variability of the threshold switching behavior in the neuron on each switching event. As a consequence, we found that the NLIF neuron exhibits a Poisson-like noise in spiking, delimiting the reliability of the information conveyed by individual NLIF neurons. To highlight neuronal information coding at a higher level, a population of noisy NLIF neurons was analyzed in regard to probability of successful information decoding given the Poisson-like noise of each neuron. The result demonstrates highly probable success in decoding in spite of large variability--due to the variability of the threshold switching behavior--of individual neurons.
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