Abstract Shared anatomical and physiological features of primary, secondary, tertiary, polysensory, and associational neocortical areas are used to formulate a novel extended hypothesis of thalamocortical circuit operation. A simplified anatomically-based model of topographically and nontopographically-projecting ('core' and 'matrix') thalamic nuclei and their differential connections with superficial, middle, and deep neocortical laminae is described. Synapses in the model are activated and potentiated according to physiologically-based rules. Features incorporated into the models include differential time courses of excitatory vs. inhibitory postsynaptic potentials, differential axonal arborization of pyramidal cells vs. interneurons, and different laminar afferent and projection patterns. Observation of the model's responses to static and time-varying inputs indicates that topographic 'core' circuits operate to organize stored memories into natural similarity-based hierarchies, whereas diffuse 'matrix' circuits give rise to efficient storage of time-varying input into retrievable sequence chains. Examination of these operations shows their relationships with well-studied algorithms for related functions, including categorization via hierarchical clustering, and sequential storage via hash-or scatter-storage. Analysis demonstrates that the derived thalamocortical algorithms exhibit desirable efficiency, scaling, and space and time cost characteristics. Implications of the hypotheses for central issues of perceptual reaction times and memory capacity are discussed. It is conjectured that the derived functions are fundamental building blocks recurrent throughout neocortex, which, through combination, give rise to powerful perceptual, motor, and cognitive mechanisms. 

A hypothesis commonly found in biological and computational studies of synaptic plasticity embodies a version of the 1949 postulate of Hebb that coactivity of pre-and postsynaptic elements results In increased efficacy of their synaptic contacts. This general proposal presaged the identification of the first and still only known long-lasting synaptic plasticity mechanisni, long-term potentiation (LTP). Yet the detailed physiology of LTP induction and expression differs in many specifics from Hebb's rule. Incorporation of these physiological LTP constraints into a simple non-Hebbian network model enabled development of "sequence detectors" that respond preferentially to the sequences on which they were trained. The network was found to have unexed capacity (e.g., 50 x 106 random sequences in a network of 10' cells), which scales linearly with network size, thereby addressing the question of memory capacity in brain circuitry of realistic size. (sufficiency). Nonetheless, there has been a trend in the literature to embrace the generality of Hebb's proposal and to emphasize its similarities to the requirements for the induction of LTP; moreover, theoretical learning rules based on the Hebb or "correlation" postulate yield networks of considerable computational power (5-10). These discrepancies between the physiological constraints on LTP induction versus the Hebb correlation rule raise the question of whether non-Hebbian properties ofLTP induction are largely extraneous to, or even impair, the behavioral and computational utility of synaptic plasticity, or whether such properties may yield learning rules that confer useful functional abilities to circuits that use them. Here we derive non-Hebbian LTP induction and expression rules from three physiological results [showing that the (simpler) Hebb rule emerges as a special case] and show that a network using these induction (learning) and expression (performance) rules acts as a highcapacity "sequence detector" that encodes and recognizes very large numbers of temporally patterned cue sequences. Induction and Expression of LTPPhysiological Characteristics of LTP Induction and Expression. Studies have established that stimulation patterns based on the 4-to 7-Hz theta electroencephalogram rhythm, which appears throughout the olfactory-hippocampal circuit in learning animals (11), are ideally suited for producing robust and stable LTP (12-15). The unlikelihood of complete synchrony of afferents requires extension of LTP induction rules to account for somewhat asynchronous inputs occurring within the envelope of a single peak of the 0 rhythm-i.e., brief (<100 ms) sequences of inputs.A Hebbian coactivity rule would predict that as asynchronous afferents arrive, increased depolarization of the target neuron over the staggered arrival times will cause later inputs to be strengthened more than earlier inputs. However, experiments using asynchronous inputs have shown that this is not the case. Experiments were done using three small inputs stimulated in a staggered s...

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