Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach i...

Modeling Spiking neural networkS on Spinnaker By

D espite an increasing amount of experimental data and deeper scientific understanding, deciphering the inner workings of biological brains remains a grand challenge. Investigations into the human brain’s microscopic structure have shown that neuron cells are the key components in the cortex. Each individual neuron is physically very much like other cells in our body, but it’s different in that...

Solving Constraint Satisfaction Problems Using Neural Networks

Learning Adaptation to Solve Constraint Satisfaction Problems

Constraint-based problems are hard combinatorial problems and are usually solved by heuristic search methods. In this paper, we consider applying a machine learning approach to improve the performance of these search-based solvers. We apply reinforcement learning in the context of Constraint Satisfaction Problems (CSP) to learn a value function, which results in a novel solving strategy. The mo...

Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

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. Howeve...