1. A system for feature detection with spiking dynamics, the system comprising: one or more processors and a memory having instructions such that when the instructions are executed, the one or more processors perform operations of: receiving an input pattern in a rate-coded domain comprising a plurality of features; translating the input pattern in the rate-coded domain into a spike-coded representation of the input pattern; transmitting the spike-coded representation of the input pattern to a feature detector layer of a feature detector network comprising a set of feature detector neurons having input weights comprising excitatory inputs and inhibitory inputs such that the feature detector neurons are arranged in an on-center (excitatory) off-surround (inhibitory) manner; activating of the set of feature detector neurons in the feature detector layer; competing of the set of feature detector neurons in the feature detector layer for the feature detector neuron whose input weights encode the input pattern best, such that each feature detector neuron in the set of feature detector neurons only excites itself, receives tonic excitation, and receives inhibition from all feature detector neurons except itself; and updating the input weights which encode frequently seen input patterns according to a learning rule selected from the group consisting of Instar learning, spike-timing-dependent plasticity (STDP) learning, and Instar STDP learning.