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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 ...
There are four sources of uncertainty regarding predictions obtained in this manner: (1) uncertainty as to whether the autoregressive model is the correct model; (2) uncertainty about the accuracy of the forecasted values that are used as lagged values in the right side of the autoregressive equation; (3) uncertainty about the true values of ...
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.
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
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 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.
Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997. [3] [4] LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient).
Baddeley's model of the phonological loop. The phonological loop (or articulatory loop) as a whole deals with sound or phonological information.It consists of two parts: a short-term phonological store with auditory memory traces that are subject to rapid decay and an articulatory rehearsal component (sometimes called the articulatory loop) that can revive the memory traces.