1. A multi-layer in-place learning developmental network, comprising: an input layer having a plurality of artificial neurons; multiple intermediate layers having a plurality of artificial neurons, each intermediate layer having artificial neurons that receive an input vector signal from neurons in previous layers; an input vector signal from neurons in the same layer and an input vector signal from neurons in subsequent layers; and an output layer having a plurality of artificial neurons, where the neurons in the network implement an in-place learning algorithm that estimates a feature vector of a given neuron by an amnesic average of input vectors weighted by corresponding response of the given neuron, where amnesic is a recursive, incremental computation of the input vector weighted by the response, such that a direction of the feature vector and a variance of signals in the region projected to the feature vector are both recursively estimated with a plasticity scheduling.