Spike-timing dependent morphological learning for a neuron with nonlinear active dendrites

It has been shown earlier that simple abstraction of a neuron with nonlinear active dendrites and binary synapses has a higher computational power than a neuron with linearly summing dendrites. However, it has only been used to classify high dimensional binary patterns of mean spike rates. In this paper, a nonlinear dendritic (NLD) neuron equipped with binary synapses that is able to learn temporal features of spike input patterns is presented. Since the synapses are binary, learning happens through formation and elimination of connections between the inputs and the dendritic branches thus modifying the structure or "morphology" of the cell. A morphological learning algorithm inspired by the `Tempotron\´-a recently proposed temporal learning algorithm-is presented in this work. Experimental results indicate that our neuron with NLD with 1-bit synapses can obtain similar accuracy as a traditional Tempotron with 4-bit synapses in classifying a population of single spike latency patterns. Hence, the proposed method is better suited for robust hardware implementation in the presence of statistical variations.