1. A method for determining a condition of a selected item, comprising: defining, by a computing device comprising a processor, a good hypertube in a neural state space, the good hypertube learned based on a plurality of good item state points obtained from at least one good item that exhibits desired characteristics, wherein the good hypertube comprises a plurality of good hypercubes, each good hypercube based on at least one good item state hyperpoint, the at least one good item state hyperpoint based on a good item state point of the plurality of good item state points; defining, by the computing device, a bad hypertube in the neural state space, the bad hypertube learned based on a plurality of bad item state points obtained from at least one bad item that exhibits undesirable characteristics, wherein the bad hypertube comprises a plurality of bad hypercubes, each bad hypercube based on at least one bad item state hyperpoint, the at least one bad item state hyperpoint based on a bad item state point of the plurality of bad item state points, and wherein the good hypertube and the bad hypertube occupy less than a totality of the neural state space; determining, by the computing device, a current item state hyperpoint in the neural state space based on a current item state point of the selected item; and quantifying the condition of the selected item as a function of a location of the current item state hyperpoint with respect to at least a portion of the good hypertube and with respect to at least a portion of the bad hypertube.