Abstract.
The brain has a hugely diverse, heterogeneous structure. By contrast, many functional
neural models are homogeneous. We compared the performance of spiking neural networks
trained to carry out difficult tasks, with varying degrees of heterogeneity. Introducing
heterogeneity in membrane and synapse time constants substantially improved task performance,
and made learning more stable and robust across multiple training methods, particularly for
tasks with a rich temporal structure. In addition, the distribution of time constants in the
trained networks closely matches those observed experimentally. We suggest that the
heterogeneity observed in the brain may be more than just the byproduct of noisy processes,
but rather may serve an active and important role in allowing animals to learn in changing
environments.