Structure induces computational function in networks with diverse types of spiking neurons

Abstract

Nature endows networks of spiking neurons in the brain with innate computing capabilities. But it has remained an open problem how the genome achieves that. Experimental data imply that the genome encodes synaptic connection probabilities between neurons depending on their genetic types and spatial distance. We show that this low-dimensional parameterization suffices for programming fundamental computing capabilities into networks of spiking neurons. However, this method is only effective if the network employs a substantial number of different neuron types. This provides an intriguing answer to the open question why the brain employs so many neuron types, many more than were used so far in neural network models. Neural networks whose computational function is induced through their connectivity structure, rather than through synaptic plasticity, are distinguished by short wire length and robustness to weight perturbations. These neural networks features are not only essential for the brain, but also for energy-efficient neuromorphic hardware.