Human performance approaches that of an ideal observer and optimal actor in some perceptual and motor tasks. These optimal abilities depend on the capacity of the cerebral cortex to store an immense amount of information and to flexibly make rapid decisions. However, behavior only approaches these limits after a long period of learning while the cerebral cortex interacts with the basal ganglia, an ancient part of the vertebrate brain that is responsible for learning sequences of actions directed toward achieving goals. Progress has been made in understanding the algorithms used by the brain during reinforcement learning, which is an online approximation of dynamic programming. Humans also make plans that depend on past experience by simulating different scenarios, which is called prospective optimization. The same brain structures in the cortex and basal ganglia that are active online during optimal behavior are also active offline during prospective optimization. The emergence of general principles and algorithms for goal-directed behavior has consequences for the development of autonomous devices in engineering applications.

Different sensory neocortical architectures share prominent architectural and operational features, including convergent feedfoward and feedback connections with both specific and nonspecific thalamic nuclei, and synaptic long-term potentiation (LTP), a suspected substrate of learning. We present a subcircuit that is composed of a broad set of these shared constituents, and that is thus common to multiple neocortical regions. The subcircuit is shown to possess the capability for high-capacity storage and recognition of arbitrary-length temporal feature sequences. Operation of the circuit is illustrated here via its application to handwritten text recognition. The model constitutes a novel hypothesis of underlying functions of sensory neocortical circuitry which, it is argued, are similar across modalities. It is worth noting that the model also suggests a novel approach to handwriting recognition, requiring no preprocessing, no size normalization, and no segmentation, as well as having low space and time complexity costs.

Analysis of the prominent anatomical and physiological features of auditory thalamus and neocortex has enabled construction of models designed to identify functionality emergent from these biological circuits.These models have recently been shown to provide powerful computational mechanisms for processing of continuous time-varying sequences such as speech; testing on speech databases has yielded positive initial results that are reported here.The model constitutes a novel hypothesis of underlying functions of auditory neocortex, and also represents a novel approach to speech processing. Generalized cortical memory modelResearch in our laboratory has been concentrating on the phenomenon of long-term potentiation (LTP) [3], which is the most likely candidate for a substrate of neocortical learning and memory.A set of simple learning rules was formulated based on physiological properties of LTP -i) synaptic weight can only increase, ii) every increase is small fixed change, and iii) low saturation threshold permits only 5-10 weight increases over the whole period of training [2] [10] .A series of models were constructed based on the known anatomical cortical features -sparse-random connectivity in the superficial cortical layers, emergence of the cortical patches defined by the radius of the local inhibition, and feedback inhibition and masking.The above variety of neocortical features specify a biologically constrained class of microcircuits, which typically perform pattern recognition or classification via competitive learning and lateral inhibition [6] [5] .Simulations of those circuits lead to efficient hardware implementations, with a proven utility for pattern recognition via efficient approximation of statistical pattern recognition methods (e.g.Bayes classifiers) [5].Key anatomical properties of the auditory model being reviewed in [16] (see also Table 1) include i) topographic (MGv) versus broadly-tuned (MGm) thalamic nuclei, convergently projecting to primary auditory cortex; local cortical circuits composed of