Evolution of Anuran Chorus Behavior: Neural and Behavioral Constraints

1992 ◽  
Vol 139 ◽  
pp. S90-S104 ◽  
Author(s):  
Peter M. Narins
Keyword(s):  
Behavioral Constraints

Interstitial Nucleus of Cajal Encodes Three-Dimensional Head Orientations in Fick-Like Coordinates

2007 ◽  
Vol 97 (1) ◽  
pp. 604-617 ◽  
Author(s):  
Eliana M. Klier ◽  
Hongying Wang ◽  
J. Douglas Crawford
Keyword(s):  
Coordinate System ◽  
Degrees Of Freedom ◽  
Three Dimensional ◽  
Semicircular Canals ◽  
Head Rotation ◽  
Head Orientation ◽  
Interstitial Nucleus Of Cajal ◽  
The Gaze ◽  
Behavioral Constraints ◽  
The Right

Two central, related questions in motor control are 1) how the brain represents movement directions of various effectors like the eyes and head and 2) how it constrains their redundant degrees of freedom. The interstitial nucleus of Cajal (INC) integrates velocity commands from the gaze control system into position signals for three-dimensional eye and head posture. It has been shown that the right INC encodes clockwise (CW)-up and CW-down eye and head components, whereas the left INC encodes counterclockwise (CCW)-up and CCW-down components, similar to the sensitivity directions of the vertical semicircular canals. For the eyes, these canal-like coordinates align with Listing’s plane (a behavioral strategy limiting torsion about the gaze axis). By analogy, we predicted that the INC also encodes head orientation in canal-like coordinates, but instead, aligned with the coordinate axes for the Fick strategy (which constrains head torsion). Unilateral stimulation (50 μA, 300 Hz, 200 ms) evoked CW head rotations from the right INC and CCW rotations from the left INC, with variable vertical components. The observed axes of head rotation were consistent with a canal-like coordinate system. Moreover, as predicted, these axes remained fixed in the head, rotating with initial head orientation like the horizontal and torsional axes of a Fick coordinate system. This suggests that the head is ordinarily constrained to zero torsion in Fick coordinates by equally activating CW/CCW populations of neurons in the right/left INC. These data support a simple mechanism for controlling head orientation through the alignment of brain stem neural coordinates with natural behavioral constraints.

scholarly journals Estimating kinetic mechanisms with prior knowledge II: Behavioral constraints and numerical tests

2018 ◽  
Vol 150 (2) ◽  
pp. 339-354 ◽  
Author(s):  
Marco A. Navarro ◽  
Autoosa Salari ◽  
Mirela Milescu ◽  
Lorin S. Milescu
Keyword(s):  
Prior Knowledge ◽  
Estimation Procedure ◽  
Kinetic Mechanism ◽  
Arbitrary Parameter ◽  
Linear Parameter ◽  
Open Probability ◽  
Behavioral Constraints ◽  
Kinetic Mechanisms ◽  
Mathematical Relationships ◽  
Optimization Mechanism

Kinetic mechanisms predict how ion channels and other proteins function at the molecular and cellular levels. Ideally, a kinetic model should explain new data but also be consistent with existing knowledge. In this two-part study, we present a mathematical and computational formalism that can be used to enforce prior knowledge into kinetic models using constraints. Here, we focus on constraints that quantify the behavior of the model under certain conditions, and on constraints that enforce arbitrary parameter relationships. The penalty-based optimization mechanism described here can be used to enforce virtually any model property or behavior, including those that cannot be easily expressed through mathematical relationships. Examples include maximum open probability, use-dependent availability, and nonlinear parameter relationships. We use a simple kinetic mechanism to test multiple sets of constraints that implement linear parameter relationships and arbitrary model properties and behaviors, and we provide numerical examples. This work complements and extends the companion article, where we show how to enforce explicit linear parameter relationships. By incorporating more knowledge into the parameter estimation procedure, it is possible to obtain more realistic and robust models with greater predictive power.

Is Savings Automation Helpful to Liquid Savings? It Depends on Whether You Have a Savings Habit

2020 ◽  
pp. 074391562095021
Author(s):  
Casey Newmeyer ◽  
Dee Warmath ◽  
Genevieve E. O’Connor ◽  
Nancy Wong
Keyword(s):  
Financial Hardship ◽  
Financial Shocks ◽  
Savings Behavior ◽  
Behavioral Constraints ◽  
Personal Savings

In general, Americans are not savers, which contributes to their inability to absorb even small financial shocks and increases their potential for financial hardship. Savings automation has been promoted as a solution to overcome the behavioral constraints (or limitations) that hinder individual savings behavior. The result has been a proliferation of automated savings programs with the goal of helping people save money without their notice as a way to overcome their tendency to consume. However, scant research has examined the efficacy of this “save people from themselves” approach. This article explores the importance of having a saver mindset, regardless of income, in the success of savings automation. Results from two studies demonstrate that the benefits of automation for liquid savings accrue at a higher rate for individuals with lower incomes and that this benefit depends on the presence of a personal savings orientation. The findings suggest that savings programs should try to build a savings habit and mindset among consumers, especially for those with lower incomes.

The City as Context: Urbanism and Behavioral Constraints in Seville.

Man ◽  
1982 ◽  
Vol 17 (1) ◽  
pp. 183
Author(s):  
David Gilmore ◽  
Irwin Press
Keyword(s):  
Behavioral Constraints ◽  
The City
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