Just-in-time connectivity for large spiking networks

Neural Comput. 2008 Nov;20(11):2745-56. doi: 10.1162/neco.2008.10-07-622.


The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.

Publication types

  • Letter
  • Research Support, N.I.H., Extramural

MeSH terms

  • Action Potentials / physiology*
  • Algorithms
  • Animals
  • Computer Simulation
  • Electronic Data Processing
  • Humans
  • Models, Neurological*
  • Nerve Net / physiology
  • Neural Networks, Computer*
  • Neurons / physiology*
  • Synapses / physiology
  • Time Factors