Visual Models
Visual Models - Related Videos
14:37
Modeling an Enzyme Active Site using Molecular Visualization Freeware
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Cited by 3 •
2021
A key skill in biomolecular modeling is displaying and annotating active sites in proteins. This technique is demonstrated using four popular free programs for macromolecular visualization: iCn3D, Jmol, PyMOL, and UCSF ChimeraX.
04:43
Visualizing Visual Adaptation
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Cited by 6 •
2017
This article describes a novel method for simulating and studying adaptation in the visual system.
06:19
Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Cited by 4 •
2025
Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
10:50
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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Cited by 2 •
2022
We summarize a workflow to computationally model a retinal neuron's behaviors in response to electrical stimulation. The computational model is versatile and includes automation steps that are useful in simulating a range of physiological scenarios and anticipating the outcomes of future in vivo/in vitro studies.
05:07
Using Looming Visual Stimuli to Evaluate Mouse Vision
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Cited by 12 •
2019
To examine mouse vision, we conducted a looming test. Mice were placed in a large square arena with a monitor on its ceiling. The looming visual stimulus consistently evoked freezing or flight reactions in the mice.
FAQs
Visual models support mathematical reasoning by establishing a correspondence between an abstract element and a visible feature. A quantity may be represented through spatial placement, a relationship through a connection, or a pattern through an arrangement. Interpreting the model means tracing these correspondences, so the learner can inspect structure before expressing or checking it formally.
Shape, position, arrangement, graph, symbol, and scale each emphasize different mathematical information. Shapes can expose geometric structure, graphs can make trends visible, and scaled diagrams can preserve relative size. Choosing among these features matters because the model should make the relevant relationship easier to inspect rather than merely decorate an already abstract statement.
Equations encode relationships symbolically, whereas a visual model can expose their spatial arrangement, trend, or structural connection. This difference helps users notice patterns and interpret data that may be difficult to recognize from equations alone. The visual representation does not replace formal mathematics; it provides another way to reason about, communicate, or develop the relationship.
Scale should be treated as part of the model's meaning, not as a cosmetic feature. In a scaled diagram, relative dimensions help communicate mathematical relationships visually. This is especially useful for geometric analysis, where inspecting size and spatial arrangement can support interpretation. The scale must therefore be understood when drawing conclusions from the representation.
Begin by identifying the quantities, operations, or connections that need to be understood. Translate those elements into suitable shapes, spatial arrangements, graphs, symbols, or scaled diagrams, then inspect the resulting correspondences and patterns. Finally, relate the visual representation back to a formal mathematical representation. This sequence supports reasoning, interpretation, and communication without separating the model from the underlying mathematics.
Visual models are useful for problem solving, data interpretation, geometric analysis, and communicating complex mathematical ideas. In problem solving, they help organize relationships; in data interpretation, they can reveal trends; and in geometry, spatial structure becomes available for inspection. Their value lies in making relevant information easier to see while supporting a mathematical conclusion.
They connect concrete experiences with formal mathematical representations. That bridge can strengthen conceptual understanding because learners can move from visible arrangements or relationships toward more formal descriptions. Visual models also support the development of mathematical models, allowing ideas to be represented and communicated before, alongside, or in support of symbolic reasoning.