Making predictions about future rewards is an important ability for primates, and its neurophysiological mechanisms have been studied extensively. One important approach is to identify neural systems that process errors related to reward prediction (i.e., areas that register the occurrence of unpredicted rewards and the failure of expected rewards). In monkeys that have learned to predict appet...

The reinforcement learning theory of the error-related negativity (ERN) holds that the impact of reward signals carried by the midbrain dopamine system modulates activity of the anterior cingulate cortex (ACC), alternatively disinhibiting and inhibiting the ACC following unpredicted error and reward events, respectively. According to a recent formulation of the theory, activity that is intrinsi...

in this research, data fusion (df) method was applied to simulate the hydrological process of taleghan reservoir daily inflow. two different df algorithms were proposed and assessed based on k-nearest neighbors (knn) algorithm. four artificial neural network models and two hammerstein-wiener (hw) models were used as the individual simulation models. comparison of the results between individual ...

Spring leaf phenology and its response to climate change have crucial effects on surface albedo, carbon balance, the water cycle of terrestrial ecosystems. Based long-term (period 1963–2014) in situ observations budburst date unfolding more than 300 deciduous woody species from 32 sites across temperate zone China, we conducted model-data comparison spatial temporal variations for spring calcul...

In this paper a new rule in cellular automata is introduced. Through phenomenological studies and basic numerical explorations, its generic dynamical behavior is outlined. Properties such as periodic and complex behavior are reported and studied at an elementary level. Further research on the rule is recommended since under certain variations (many of which are reported) this rule can be a reli...