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. 2010 Apr 20;4(4):295–313. doi: 10.1007/s11571-010-9110-4

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Fig. 2 — A processing unit, PU(n), with a feature subvector index n, comprising an Orthogonal Expander, a label SPD (subjective probability distribution) Estimator, a Spike Generator, a GECM (general expansion correlation matrix) Adjuster, and a storage of and . PU(n) has essentially two functions, retrieving a “point estimate” or a sequence of “point estimates” (i.e., spike trains) of the label of a feature subvector from the memory, GECMs, and learning a feature subvector and its label that is either provided from outside the PU (in supervised learning) or generated by the PU itself (in unsupervised learning)

Inline graphic — A processing unit, PU(n), with a feature subvector index n, comprising an Orthogonal Expander, a label SPD (subjective probability distribution) Estimator, a Spike Generator, a GECM (general expansion correlation matrix) Adjuster, and a storage of and . PU(n) has essentially two functions, retrieving a “point estimate” or a sequence of “point estimates” (i.e., spike trains) of the label of a feature subvector from the memory, GECMs, and learning a feature subvector and its label that is either provided from outside the PU (in supervised learning) or generated by the PU itself (in unsupervised learning)