ReviewF1000Research2016
Dynamical systems, attractors, and neural circuits.
Review in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed.
- Diffusive memristors in the edge of chaos.Nature communications · 2026Article
- Spatiotemporal asymmetries on brain energy landscape uncover system entrapment related to depression severity.Nature communications · 2026Article
- Explicit error coding can mediate gain recalibration in continuous bump attractor networks.Nature communications · 2025Article
- Harnessing the analog computing power of regulatory networks with the Regulatory Network Machine.iScience · 2025Article
- Stereotyped Spatiotemporal Dynamics of Spontaneous Activity in Visual Cortex Prior to Eye Opening.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Beyond Mechanism-Extending Our Concepts of Causation in Neuroscience.The European journal of neuroscience · 2025Review
- A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection.Nature communications · 2024Article
- A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection.bioRxiv : the preprint server for biology · 2024Article
- Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis.Sensors (Basel, Switzerland) · 2024Article
- Reconstructing computational system dynamics from neural data with recurrent neural networks.Nature reviews. Neuroscience · 2023Review
- Anxiety and depression: A top-down, bottom-up model of circuit function.Annals of the New York Academy of Sciences · 2023Review
- Ascending neurons convey behavioral state to integrative sensory and action selection brain regions.Nature neuroscience · 2023Article
- Article
- Article
- Conceptual framework for neuronal ensemble identification and manipulation related to behavior using calcium imaging.Neurophotonics · 2022Review
- It's about time: Linking dynamical systems with human neuroimaging to understand the brain.Network neuroscience (Cambridge, Mass.) · 2022Article
- Understanding ethanol's acute effects on medial prefrontal cortex neural activity using state-space approaches.Neuropharmacology · 2021Review
- The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states.Nature communications · 2021Article
- Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics.Nature neuroscience · 2021Review
- The function of groups of neurons changes from moment to moment.Current opinion in physiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.
Indexed as
Identifiers
What OpenQuestion holds
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.