Making spiking neurons more succinct with multi-compartment models

Spiking neurons consume energy for each spike they emit. Reducing the firing rate of each neuron - - without sacrificing relevant information content - - is therefore a critical constraint for energy efficient networks of spiking neurons in biology and neuromorphic hardware alike. The inherent complexity of biological neurons provides a possible mechanism to realize a good trade-off between these two conflicting objectives: multi-compartment neuron models can become selective to highly specific input patterns, and thus learn to produce informative yet sparse spiking codes. In this paper, I motivate the operation of a simplistic hierarchical neuron model by analogy to decision trees, show how they can be optimized using a modified version of the greedy decision tree learning rule, and analyze the results for a simple illustrative binary classification problem.