  In this paper, a neuron with nonlinear dendrites (NNLD) and binary synapses
that is able to learn temporal features of spike input patterns is considered.
Since binary synapses are considered, learning happens through formation and
elimination of connections between the inputs and the dendritic branches to
modify the structure or "morphology" of the NNLD. A morphological learning
algorithm inspired by the 'Tempotron', i.e., a recently proposed temporal
learning algorithm-is presented in this work. Unlike 'Tempotron', the proposed
learning rule uses a technique to automatically adapt the NNLD threshold during
training. Experimental results indicate that our NNLD with 1-bit synapses can
obtain similar accuracy as a traditional Tempotron with 4-bit synapses in
classifying single spike random latency and pair-wise synchrony patterns.
Hence, the proposed method is better suited for robust hardware implementation
in the presence of statistical variations. We also present results of applying
this rule to real life spike classification problems from the field of tactile
sensing.
