The theory is motivated by the observed similarities between the brain structures (especially neocortical tissue) which are used for a wide range of behaviours available to mammals. The theory posits that the remarkably uniform physical arrangement of cortical tissue reflects a single principle or algorithm which underlies all cortical information processing. The basic processing principle is hypothesized to be a feedback/recall loop which involves both cortical and extra-cortical participation (the latter from the thalamus and the hippocampus in particular).
The memory-prediction framework provides a unified basis for thinking about the adaptive control of complex behavior. Although certain brain structures are identified as participants in the core 'algorithm' of prediction-from-memory, these details are less important than the set of principles that are proposed as basis for all high-level cognitive processing.
The central concept of the memory-prediction framework is that bottom-up inputs are matched in a hierarchy of recognition, and evoke a series of top-down expectations encoded as potentiations. These expectations interact with the bottom-up signals to both analyse those inputs and generate predictions of subsequent expected inputs. Each hierarchy level remembers frequently observed temporal sequences of input patterns and generates labels or 'names' for these sequences. When an input sequence matches a memorized sequence at a given layer of the hierarchy, a label or 'name' is propagated up the hierarchy - thus eliminating details at higher levels and enabling them to learn higher-order sequences. This process produces increased invariance at higher levels. Higher levels predict future input by matching partial sequences and projecting their expectations to the lower levels. However, when a mismatch between input and memorized/predicted sequences occurs, a more complete representation propagates upwards. This causes alternative 'interpretations' to be activated at higher levels, which in turn generates other predictions at lower levels.
Consider, for example, the process of vision. Bottom-up information starts as low-level retinal signals (indicating the presence of simple visual elements and contrasts). At higher levels of the hierarchy, increasingly meaningful information is extracted, regarding the presence of lines, regions, motions, etc. Even further up the hierarchy, activity corresponds to the presence of specific objects - and then to behaviours of these objects. Top-down information fills in details about the recognized objects, and also about their expected behaviour as time progresses.
The sensory hierarchy induces a number of differences between the various layers. As one moves up the hierarchy, representations have increased:
The relationship between sensory and motor processing is an important aspect of the basic theory. It is proposed that the motor areas of cortex consist of a behavioural hierarchy similar to the sensory hierarchy, with the lowest levels consisting of explicit motor commands to musculature and the highest levels corresponding to abstract prescriptions (e.g. 'resize the browser'). The sensory and motor hierarchies are tightly coupled, with behaviour giving rise to sensory expectations and sensory perceptions driving motor processes.
Finally, it is important to note that all the memories in the cortical hierarchy have to be learnt - this information is not pre-wired in the brain. Hence, the process of extracting this representation from the flow of inputs and behaviours is theorized as a process that happens continually during cognition.
That is, the evolutionary purpose of the brain is to predict the future, in admittedly limited ways, so as to change it.
Early computer experiments with Bayesian learning in feed-forward hierarchical state machines seem to indicate that this model generates behavior very similar to that of real organisms.
The hierarchies described above are theorized to occur primarily in mammalian neocortex. In particular, neocortex is assumed to consist of a large number of columns (as surmised also by Mountcastle from anatomical and theoretical considerations). Each column is attuned to a particular feature at a given level in a hierarchy. It receives bottom-up inputs from lower levels, and top-down inputs from higher levels. (Other columns at the same level also feed into a given column, and serve mostly to inhibit the activiation exclusive representations.) When an input is recognized - that is, acceptable agreement is obtained between the bottom-up and top-down sources - a column generates outputs which in turn propagate to both lower and higher levels.
The memory-prediction framework explains a number of psychologically salient aspects of cognition. For example, the ability of experts in any field to effortlessly analyze and remember complex problems within their field is a natural consequence of their formation of increasingly refined conceptual hierarchies. Also, the procession from 'perception' to 'understanding' is readily understandable as a result of the matching of top-down and bottom-up expectations. Mismatches, in contrast, generate the exquisite ability of biological cognition to detect unexpected perceptions and situations. (Deficiencies in this regard are a common characteristic of current approaches to artificial intelligence.)
Besides these subjectively satisfying explanations, the framework also makes a number of testable predictions. For example, the important role that prediction plays throughout the sensory hierarchies calls for anticipatory neural activity in certain cells throughout sensory cortex. In addition, cells that 'name' certain invariants should remain active throughout the presence of those invariants, even if the underlying inputs change. The predicted patterns of bottom-up and top-down activity - with former being more complex when expectations are not met - may be detectable, for example by functional magnetic resonance imaging (fMRI).
Although these predictions are not highly specific to the proposed theory, they are sufficiently unambiguous to make verification or rejection of its central tenets possible. See On Intelligence for details on the predictions and findings.
By design, the current theory builds on the work of numerous neurobiologists, and it may be argued that most of these ideas have already been proposed by researchers such as Grossberg and Mountcastle. On the other hand, the novel separation of the conceptual machinery of bidirectional processing and invariant recognition from the biological details of neural layers, columns and structures lays the foundation for abstract thinking about a wide range of cognitive processes.
The most significant limitation of this theory is its current lack of detail. For example, the concept of invariance plays a crucial role; Hawkins posits "name cells" for at least some of these invariants. ( See also Neural ensemble#Encoding for grandmother neurons which perform this type of function, and mirror neurons for a somatosensory system viewpoint. ) But it is far from obvious how to develop a mathematically rigorous definition, which will carry the required conceptual load across the domains presented by Hawkins. Similarly, a complete theory will require credible details on both the short-term dynamics and the learning processes that will enable the cortical layers to behave as advertised.