Learning encoding and decoding filters for data representation with a spiking neuron

Data representation methods related to ICA and sparse coding have successfully been used to model neural representation. However, they are highly abstract methods, and the neural encoding does not correspond to a detailed neuron model. This limits their power to provide deeper insight into the sensory systems on a cellular level. We propose here data representation where the encoding happens with a spiking neuron. The data representation problem is formulated as an optimization problem: encode the input so that it can be decoded from the spike train, and optionally, so that energy consumption is minimized. The optimization leads to a learning rule for the encoder and decoder which features synergistic interaction: the decoder provides feedback affecting the plasticity of the encoder while the encoder provides optimal learning data for the decoder.