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This width dependence allows explaining gate voltage matching behavior in the sub-threshold area used in low power analog applications.
Decision-making models to reproduce the matching behavior have been proposed[9], [16], [17], and recent computational studies pointed out possible origins of matching behavior in biological neural systems[18], [19].
They find matching behavior in the animals.
A question of particular interest is why the animals exhibited matching behavior in this task.
We observed matching behavior in a task in which more reward could be harvested if animals over-matched the reward proportions.
Furthermore, at the level of mechanistic implementation, a biophysically based neural model grounded in reinforcement learning was also shown to reproduce matching behavior in a baiting task.
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Virtual environments are only useful for health-related research, however, if behavior in the virtual world validly matches behavior in the real world.
Our benchmark calibration follows Bergoeing et al. (2002) in choosing behavioral parameters to match household behavior in a period in which we are not interested, in this case 1970 1974, and we test the model by seeing how well it matches behavior in the period in which we are interested, in this case 1975 2015.
In this study, we propose a view that unifies matching behavior into the general computational framework of reward maximization.
The finding that matching behavior is observed in a task that does not impose it provides important insights into the nature of matching behavior.
Under both mechanisms, the signal transiently rises, reaching a peak around 15 20 min after NGF stimulation and then eventually decreases and settles in a steady state; this behavior matches behavior of ppErk concentration observed in experiments upon NGF stimulation.
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