We introduce a new set of mechanisms for tracking entities through videos, at substantially less expense than required by standard methods. The approach combines inexpensive initial processing of individual frames together with integration of information across long time spans (multiple frames), resulting in the recognition and tracking of spatially and temporally contiguous entities, rather than focusing on the individual pixels that comprise those entities.

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

Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably "neat" form, e.g., newspaper stories and other edited texts. However, a great deal of natural language text, e.g., memos, rough drafts, conversation transcripts, etc., have features that differ significantly from "neat" texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc. Our solution to these problems is to make use of expectations, based both on knowledge of surface English and on world knowledge of the situation being described. These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (ellipsis), and resolve referents (anaphora). This method of using expectations to aid the understanding of "scruffy" texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages.