Investigations apropos of the passage in Ovid to which we shall ultimately come have revealed that one kind of Latin genitive at least is still far from satisfactorily charted by authorities more eminent even than M'Turk. This is the genitive of material. More often than not grammarians and commentators do not distinguish this usage from the genitive of definition. So for instance at Ovid, Met. 3. 315 the phrase lactis alimenta is identified by Bomer ad loc. and by H. J. (...) Roby, ii. § 1302) as genitive of definition. This is correct as far as it goes, in the sense that lactis defines the alimenta; but the definition is what they were made of, their material. So again at Met. I. III flumina iam lactis, iam ftumina nectaris ibant the rivers consisted of milk and nectar. This passage, however, was classified by J. N. Madvig, p. 249) under expressions involving number, measure or quantity. This is not unreasonable, but Madvig's examples illustrate the need for care in drawing the line. They include, for instance, aceruus frumenti and uini tres amphorae. Now a heap may be thought of as containing its constituents, but it also consists o/them, which is not true of a vessel and its contents. Thus, though there is some semantic justification for including both these expressions, as Madvig did, under the ‘measure or quantity’ rubric, they are nevertheless grammatically distinct. The fluidity of the borderlines in this area is evident also in flumina lactis, an undeniable genitive of material which is, however, clearly analogous to a not uncommon class of expressions such as riuus/fons aquae, lapidum imber, uolumina fumi, campus harenae, silicum uenae, ὔδατος ῥος, κρνη ὔδατος, ργρου πηγ, etc., in which the notion of quantity as well as composition is present. (shrink)

The perceptual circularity demonstrated by R. Shepard with respect to hue turns out to be a sphericity of color perception based on color excitation vectors of neuronal level. The spherical color model implicitly contains information concerning generalization under color learning. Subjective color differences are “computed” in neuronal nets being represented by amplitudes of evoked potentials triggered by color change. [Shepard].

Edelman suggests that any shape is encoded by an excitation vector with components corresponding to excitations of corresponding neuronal modules. This results in discrimination of stimuli in a shape space of low dimensionality. Similar vector encoding is present in color vision. Red-green, blue-yellow, bright and dark neurons are modules that represent a number of different color stimuli in color space of low dimensionality. Vector encoding allows effective computation of color differences and color similarities. Such a neuronal vector-encoding approach has also (...) been applied to the perception of visual movement, line orientation, and stereopsis. (shrink)

A formal neuron has been studied mathematically. The spiking behaviour of a single neuron has been considered and the influence of the other neurons has been replaced by an average activity level. Four different kinds of spiking behaviour are predicted by the model: B (bursts), C (continuous), P (periodic) and S (silent) neurons and several real neurons can be classified within these four categories. Some properties of the spiking neuron are calculated: 1) the time between spikes, 2) the spike train (...) length and 3) the silent time. Because these magnitudes can be measured in the laboratory, an experimental validation of the model is proposed. (shrink)

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