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.