Article Figures & Data
Figures
- Figure 1.
The modeling exercise. Models interact with experiments through the generation of novel model-based experimental predictions. Experimental data will in turn provide new unexplained data and hypotheses that call for new or refined models. Note that modelers do not necessarily need to test their own experimental predictions or collect their own unexplained data; but good modelers should interact with experimentalists. Many good experiments come from modelers annoyingly asking for data.
- Figure 2.
Mechanical watch. Even knowing what it does, its inner workings are far from trivial. Imagine an archeologist finding one of those and not knowing what this is for.
- Figure 3.
Iterative view of the first steps of the modeling exercise. Consecutive thought processes often identify lack, omissions, imprecisions, and uncertainties that require the modeler to go back and refine their thoughts. This is true when framing the question and independently applies during model implementation. Note that these two processes are serial. One should not start the implementation process without having fully satisfied all model framing criteria and steps. Solid arrows denote direct transitions/dependencies; dashed arrows stand for iterative reconsideration. Once a phenomenon/question is identified, required ingredients and literature review are conducted, which ideally leads to a potential experimental test. If no such test can be found, maybe the question needs reformulating. One should be able to identify specific hypotheses; otherwise there is a lack of specificity/precision in the question that needs to be revisited. Toolkit selection, drafting, and implementation of the model involve iterative unit testing. Unit testing can identify pitfalls in drafting or even in the choice of the toolkit (less frequently) that require adjustment of the model plan.
- Figure 4.
Model diagrams. A, Mechanical elements of a mechanical clock. B, Flow diagram equivalent of the mechanical clock. C, Dynamical systems equivalent to the mechanical clock. B, C, Note: inputs are different between full model (B) and reduced model (C). Exemplary variables (tilted text) passed between model elements also differ in nature.
- Figure 5.
Updated version of Robinson’s simple saccade model (Scudder, 1988). Saccade target shift (
) is compared to an internal estimate of saccade progression computed through the resettable neural integrator [
, suggested by Scudder (1988), not Robinson] to provide a motor error (err). Based on circumstantial evidence, Robinson’s insight led him to postulate the pulse generator (
) to provide a desired eye velocity drive (v*). This pulse command was scaled to match the eye plant dynamics (gain,
) and provided the saccade drive. However, Robinson recognized that viscoelastic forces would pull the eye back to primary position if not actively compensated for. This is how he proposed the neural integrator (
) to provide a tonic drive that overcomes the viscoelastic forces. Tonic and phasic drives add up and are sent to extraocular muscles of the eye plant that he modeled as a second-order system to move the eye (E). Red labels are mappings of individual computations to specific brain areas. CBLM, Cerebellum; BG, basal ganglia; SC, superior colliculus; MLF, medial longitudinal fasciculus; MVN, medial vestibular nucleus; NPH, nucleus prepositus hypoglossi; MN, motor neurons. Gray boxes indicate Robinson’s innovations. Black box denotes a later modification of Robinson’s model by Scudder (1988), included here for correctness.
Tables
Question-answer What How Why Phenomenon 1 Tick/s Loud for 100 ms then silent for 900 ms Whatever that thingamajig is called Timekeeping. Duh. Gears exist Notches on circles More notches = slower rotation Translate faster rotation / slower rotation 1 Rotation/h Periodicity 120 notches on hour ring, 2 on second ring Because an hour is a useful division of the day
In this issue
