1. A spatiotemporal associative memory device, comprising: a plurality of electronic neurons having a layered neural net relationship with bi-directional synaptic connectivity; wherein, in a training phase, an input presentation protocol is applied to train the plurality of electronic neurons, the input presentation protocol comprising: partitioning the plurality of electronic neurons into multiple clusters, each cluster comprising approximately the same number of neurons; and presenting spatiotemporal patterns to the plurality of electronic neurons, one at a time, over fixed time intervals of a first pre-determined length, each presentation of a particular spatiotemporal pattern followed by a silent period of a second pre-determined length, and each presentation of a particular spatiotemporal pattern manifesting as a flurry of activity over a fixed time interval of the first pre-determined length, the activity confined mainly to a particular cluster of the multiple clusters, thereby allowing association of particular spatiotemporal patterns with particular clusters of the multiple clusters; and wherein the plurality of electronic neurons are configured to: detect the presence of a spatiotemporal pattern in an input data stream; extract the spatiotemporal pattern; based on learning rules, store the spatiotemporal pattern in the plurality of electronic neurons, wherein the learning rules provide a level of conductance for synaptic interconnections between the plurality of electronic neurons as a function of the stored spatiotemporal pattern; recall the stored spatiotemporal pattern by retrieving the stored spatiotemporal pattern when presented with a version of the stored spatiotemporal pattern, wherein the stored spatiotemporal pattern is provided as output for display on a display device; and combining the stored spatiotemporal pattern with at least one other stored spatiotemporal pattern to form a complex composite representing one of a permutation and a combination of multiple spatiotemporal patterns; wherein the plurality of electronic neurons comprise a plurality of electronic neuron layer sheets including a first electronic neuron layer sheet and a second electronic neuron layer sheet; wherein each electronic neuron layer sheet comprises at least one spiking electronic neuron layer including a population of electronic neurons; wherein a first neuron of the first electronic neuron layer sheet is connected to a second neuron of the second electronic neuron layer sheet that corresponds topographically with a location of the first neuron, and the first neuron is further connected to one or more other neurons of the second electronic neuron layer sheet within a pre-determined radius of the second neuron; and wherein different receptive field profiles are used for each neuron layer sheet, such that the electronic neuron layer sheets detect, in parallel, different pattern features in the input data stream.