Visualization of currents in neural models with similar behavior and different conductance densities
Open accessPublished version of a preprint
Abstract
Conductance-based models of neural activity produce large amounts of data that can be hard to visualize and interpret. We introduce visualization methods to display the dynamics of the ionic currents and to display the models’ response to perturbations. To visualize the currents’ dynamics, we compute the percent contribution of each current and display them over time using stacked-area plots. The waveform of the membrane potential and the contribution of each current change as the models are perturbed. To represent these changes over a range of the perturbation control parameter, we compute and display the distributions of these waveforms. We illustrate these procedures in six examples of bursting model neurons with similar activity but that differ as much as threefold in their conductance densities. These visualization methods provide heuristic insight into why individual neurons or networks with similar behavior can respond widely differently to perturbations.
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In model neuron A there is virtually no A current, while in model neuron B the A current is quite large.
“Note that in model neuron A there is virtually no A current (purple), while in model neuron B the A current is quite large.”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [42]
In Results, under “The maximal conductances do not fully predict the currentscapes” (page unavailable), the paper reports that A-current contributions differ among its model bursters. It does not state that model A has virtually no A current while model B has a large A current, so the specific comparison is not verified by our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredUnder 'control' conditions, the models are degenerate and show very similar bursting behavior.
“Despite this, under 'control' conditions, the models are degenerate and show very similar bursting behavior”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [42]
In computational models it is feasible to examine the structures of the parameter spaces that contribute to an output or a physiological state.
“In computational models it is feasible to examine the structures of the parameter spaces that contribute to an output or a physiological state [31,42,46,47].”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [42]
Models A and B show similar behavior under control conditions, but the contributions of different currents to their control activity are different.
“While Models A and B show similar behavior under control conditions, the contributions of different currents to their control activity are different.”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [42]
Model neuron A and model neuron B have the same constellation of ion channels, but with different values of their maximal conductances.
“Model neuron A and model neuron B have the same constellation of ion channels, but with different values of their maximal conductances.”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [42]
At 50% deletion of one Ca2+ current, the models are again degenerate and produce similar behavior.
“it is striking that at 50% deletion the models are again degenerate, and produce similar behavior.”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [42]
At 75% deletion of one Ca2+ current, the two models are noticeably different.
“Although the 75% panel is noticeably different in the two models”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [42]
At 25% and 0% deletion of one Ca2+ current, the two models behave markedly different.
“Nonetheless at 25% and 0% ,the two models behave markedly different.”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [42]
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Self-citation
- Authors
- Alonso, L. M., & Marder, E.
- Affiliations
- Brandeis University (US)
- Corresponding author
- Alonso, Leandro M
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-01-31
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC6395073
- OpenAlex
- W2911737192
- Cited by
- 119
- References
- 2
- Citations by year
- 2026: 22 · 2025: 16 · 2024: 20 · 2023: 17 · 2022: 14 · 2021: 17 · 2020: 11 · 2019: 1
- FWCI
- 6.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R35 NS097343, MH046742, T32 NS07292)
- Swartz Foundation (2017)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
- Keywords
- conductance-based neuron model, perturbation response, bursting neurons
- MeSH terms
- Action Potentials, Humans, Ion Channels, Membrane Potentials, Models, Theoretical, Neurons, Calcium Channels, Potassium Channels, Sodium Channels
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