ReviewJournal of neurophysiology2016
Neurons as oscillators.
Review in Journal of neurophysiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
35 citing papers in PubMed.
- Article
- Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications.Chemical reviews · 2026Review
- Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits.Neural computation · 2026Article
- Neuronal and network resonance in the external globus pallidus.Journal of neurophysiology · 2025Article
- Theta-gamma coupling: nonlinearity as a universal cross-frequency coupling mechanism.Frontiers in behavioral neuroscience · 2025Article
- Heat-assisted neuromorphic computing.Nature materials · 2024Article
- Impact of Unitary Synaptic Inhibition on Spike Timing in Ventral Tegmental Area Dopamine Neurons.eNeuro · 2024Article
- Synchronization of delayed coupled neurons with multiple synaptic connections.Cognitive neurodynamics · 2024Article
- Resilience of the slow component in timescale-separated synchronized oscillators.Frontiers in network physiology · 2024Article
- Computing with oscillators from theoretical underpinnings to applications and demonstrators.Npj unconventional computing · 2024Review
- Active oscillations in microscale navigation.Animal cognition · 2023Review
- Propagation of Oscillations in the Indirect Pathway of the Basal Ganglia.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Engineering spatiotemporal patterns: information encoding, processing, and controllability in oscillator ensembles.Biomedical physics & engineering express · 2023Article
- Article
- Optimal synchronization in pulse-coupled oscillator networks using reinforcement learning.PNAS nexus · 2023Article
- [Frequency-Specific Alterations of Spontaneous Brain Activity in First-Episode Drug-Naïve Schizophrenia].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2023Article
- Modeling of cerebellar transcranial electrical stimulation effects on hand tremor in Parkinson's disease.Frontiers in aging neuroscience · 2023Article
- Editorial: Role of brain oscillations in neurocognitive control systems.Frontiers in systems neuroscience · 2023Article
- A framework for macroscopic phase-resetting curves for generalised spiking neural networks.PLoS computational biology · 2022Article
- Satiety Associated with Calorie Restriction and Time-Restricted Feeding: Central Neuroendocrine Integration.Advances in nutrition (Bethesda, Md.) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Regularly spiking neurons can be described as oscillators. In this article we review some of the insights gained from this conceptualization and their relevance for systems neuroscience. First, we explain how a regularly spiking neuron can be viewed as an oscillator and how the phase-response curve (PRC) describes the response of the neuron's spike times to small perturbations. We then discuss the meaning of the PRC for a single neuron's spiking behavior and review the PRCs measured from a variety of neurons in a range of spiking regimes. Next, we show how the PRC can be related to a number of common measures used to quantify neuronal firing, such as the spike-triggered average and the peristimulus histogram. We further show that the response of a neuron to correlated inputs depends on the shape of the PRC. We then explain how the PRC of single neurons can be used to predict neural network behavior. Given the PRC, conduction delays, and the waveform and time course of the synaptic potentials, it is possible to predict neural population behavior such as synchronization. The PRC also allows us to quantify the robustness of the synchronization to heterogeneity and noise. We finally ask how to combine the measured PRCs and the predictions based on PRC to further the understanding of systems neuroscience. As an example, we discuss how the change of the PRC by the neuromodulator acetylcholine could lead to a destabilization of cortical network dynamics. Although all of these studies are grounded in mathematical abstractions that do not strictly hold in biology, they provide good estimates for the emergence of the brain's network activity from the properties of individual neurons. The study of neurons as oscillators can provide testable hypotheses and mechanistic explanations for systems neuroscience.
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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.