ReviewJournal of neurophysiology2016
Is realistic neuronal modeling realistic?
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 51 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
51 citing papers in PubMed.
- Toward Reference-Grade neuron models.Communications biology · 2026Article
- Accurately fitting biophysical neuron models to experimental voltage data enabled by meta-learning.Research square · 2026Article
- Neuronal excitability and parameter variability in the Hodgkin-Huxley model.PLoS computational biology · 2026Article
- Dynamic cholinergic signaling differentially desynchronizes cortical microcircuits dependent on modulation rate and network connectivity.PLoS computational biology · 2026Article
- How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells.Frontiers in computational neuroscience · 2026Article
- Morphological details contribute to neuronal response variability within the same cell type.Frontiers in cellular neuroscience · 2026Article
- Rapid, interpretable data-driven models of neural dynamics using recurrent mechanistic models.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A Spike Train Production Mechanism Based on Intermittency Dynamics.Entropy (Basel, Switzerland) · 2025Article
- Nociceptor sodium channels shape subthreshold phase, upstroke, and shoulder of action potentials.The Journal of general physiology · 2025Article
- Quantitative prediction of intracellular dynamics and synaptic currents in a small neural circuit.Frontiers in computational neuroscience · 2025Article
- System-level brain modeling.Frontiers in computational neuroscience · 2025Article
- Would you publish unrealistic models?Biological cybernetics · 2024Article
- Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.PLoS computational biology · 2024Article
- Impact on backpropagation of the spatial heterogeneity of sodium channel kinetics in the axon initial segment.PLoS computational biology · 2024Article
- Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families.PloS one · 2024Article
- Magnetoelectric Nanodiscs Enable Wireless Transgene-Free Neuromodulation.bioRxiv : the preprint server for biology · 2023Article
- Neurobiology and Changing Ecosystems: Mechanisms Underlying Responses to Human-Generated Environmental Impacts.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- A biophysical perspective on the resilience of neuronal excitability across timescales.Nature reviews. Neuroscience · 2023Review
- A Fundamental Inequality Governing the Rate Coding Response of Sensory Neurons.Biological cybernetics · 2023Article
- Cold and warmth intensify pain-linked sodium channel gating effects and persistent currents.The Journal of general physiology · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models.
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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.