A theoretical basis for efficient computations with noisy spiking
Z. Jonke, S. Habenschuss, and W. Maass
Network of neurons in the brain apply unlike processors in our current
generation of computer hardware an event-based processing strategy, where
short pulses (spikes) are emitted sparsely by neurons to signal the
occurrence of an event at a particular point in time. Such spike-based
computations promise to be substantially more power-efficient than
traditional clocked processing schemes. However it turned out to be
surprisingly difficult to design networks of spiking neurons that are able to
carry out demanding computations. We present here a new theoretical framework
for organizing computations of networks of spiking neurons. In particular, we
show that a suitable design enables them to solve hard constraint
satisfaction problems from the domains of planning/optimization and
verification/logical inference. The underlying design principles employ noise
as a computational resource. Nevertheless the timing of spikes (rather than
just spike rates) plays an essential role in the resulting computations.
Furthermore, one can demonstrate for the Traveling Salesman Problem a
surprising computational advantage of networks of spiking neurons compared
with traditional artificial neural networks and Gibbs sampling. The
identification of such advantage has been a well-known open problem
Reference: Z. Jonke, S. Habenschuss, and W. Maass.
A theoretical basis for efficient computations with noisy spiking neurons.
arXiv.org, arXiv:1412.5862, 2014.