Log In Sign Up

A theoretical basis for efficient computations with noisy spiking neurons

by   Zeno Jonke, et al.

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.


page 1

page 2

page 3

page 4


Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

Biological neurons communicate with a sparing exchange of pulses - spike...

Efficient Computation in Adaptive Artificial Spiking Neural Networks

Artificial Neural Networks (ANNs) are bio-inspired models of neural comp...

The thermodynamic temperature of a rhythmic spiking network

Artificial neural networks built from two-state neurons are powerful com...

Long short-term memory and Learning-to-learn in networks of spiking neurons

Networks of spiking neurons (SNNs) are frequently studied as models for ...

Spiking neurons with short-term synaptic plasticity form superior generative networks

Spiking networks that perform probabilistic inference have been proposed...

Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers

Solving constraint satisfaction problems (CSPs) is a notoriously expensi...

A superconducting nanowire spiking element for neural networks

As the limits of traditional von Neumann computing come into view, the b...