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In the model, interaction between the medial temporal hippocampus (MTH) and multiple areas in the neocortex lead to the formation of a cortical trace which represents a single memory. While this MTH-neocortex interaction is initially required to maintain the memory trace, the model predicts that over time the importance of the MTH becomes ...
Causes of such memory errors may be due to certain cognitive factors, such as spreading activation, or to physiological factors, including brain damage, age or emotional factors. Furthermore, memory errors have been reported in individuals with schizophrenia and depression. The consequences of memory errors can have significant implications.
This phenomenon is commonly referred to as the testing effect. [18] Another study showed that when lists are tested immediately after study, the last couple of pairs are remembered best. After a five-second delay, the recall of recently studied words diminishes. However, word pairs at the beginning of a list still show better recall.
There are two major types of problems in uncertainty quantification: one is the forward propagation of uncertainty (where the various sources of uncertainty are propagated through the model to predict the overall uncertainty in the system response) and the other is the inverse assessment of model uncertainty and parameter uncertainty (where the ...
Model A reduces to the models studied in [3] [4] depending on the choice of the activation function, model B reduces to the model studied in, [1] model C reduces to the model of. [ 5 ] General systems of non-linear differential equations can have many complicated behaviors that can depend on the choice of the non-linearities and the initial ...
The free energy principle is a theoretical framework suggesting that the brain reduces surprise or uncertainty by making predictions based on internal models and updating them using sensory input. It highlights the brain's objective of aligning its internal model and the external world to enhance prediction accuracy.
Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models , and other sequence learning methods.
The Levels of Processing model, created by Fergus I. M. Craik and Robert S. Lockhart in 1972, describes memory recall of stimuli as a function of the depth of mental processing. More analysis produce more elaborate and stronger memory than lower levels of processing. Depth of processing falls on a shallow to deep continuum.