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The memory-prediction framework is a theory of brain function created by Jeff Hawkins and described in his 2004 book On Intelligence.This theory concerns the role of the mammalian neocortex and its associations with the hippocampi and the thalamus in matching sensory inputs to stored memory patterns and how this process leads to predictions of what will happen in the future.
Predictive coding was initially developed as a model of the sensory system, where the brain solves the problem of modelling distal causes of sensory input through a version of Bayesian inference. It assumes that the brain maintains an active internal representations of the distal causes, which enable it to predict the sensory inputs. [ 5 ]
As early as the 1860s, with the work of Hermann Helmholtz in experimental psychology, the brain's ability to extract perceptual information from sensory data was modeled in terms of probabilistic estimation. [5] [6] The basic idea is that the nervous system needs to organize sensory data into an accurate internal model of the outside world.
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.
The Oscillator Based Associative Recall (OSCAR) Model was proposed by Browne, Preece and Hulme in 2000 [7] The OSCAR Model is another cue driven model of memory. In this model, the cues work as a pointer to a memory’s position in the mind. Memories themselves are stored as context vectors on what Brown calls the oscillator part of the theory.
Knowing utilizes semantic memory that requires perceptually based, data-driven processing. Knowing is the result of shallow maintenance rehearsal that can be influenced by many of the same aspects as semantic memory. Remember and know responses are quite often differentiated by their functional correlates in specific areas in the brain.
The forgetting curve hypothesizes the decline of memory retention in time. This curve shows how information is lost over time when there is no attempt to retain it. [ 1 ] A related concept is the strength of memory that refers to the durability that memory traces in the brain .
Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee , HTM is primarily used today for anomaly detection in streaming data.