ArticleFrontiers in neuroscience2016
Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Neural sampling from cognitive maps enables goal-directed imagination and planning.Nature machine intelligence · 2026Article
- Solving Max-Cut Problem Using Spiking Boltzmann Machine Based on Neuromorphic Hardware with Phase Change Memory.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Local prediction-learning in high-dimensional spaces enables neural networks to plan.Nature communications · 2024Article
- Efficient optimization with higher-order ising machines.Nature communications · 2023Article
- Cortical oscillations support sampling-based computations in spiking neural networks.PLoS computational biology · 2022Article
- Spike-induced ordering: Stochastic neural spikes provide immediate adaptability to the sensorimotor system.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- A Spiking Neuron and Population Model Based on the Growth Transform Dynamical System.Frontiers in neuroscience · 2020Article
- Deterministic networks for probabilistic computing.Scientific reports · 2019Article
- Temporal-specific complexity of spiking patterns in spontaneous activity induced by a dual complex network structure.Scientific reports · 2019Article
- Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks.Frontiers in neuroscience · 2019Article
- Making BREAD: Biomimetic Strategies for Artificial Intelligence Now and in the Future.Frontiers in neuroscience · 2019Review
- A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks.Frontiers in neuroscience · 2019Article
- Analog Approach to Constraint Satisfaction Enabled by Spin Orbit Torque Magnetic Tunnel Junctions.Scientific reports · 2018Article
- Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks.Neural computation · 2018Article
- Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems.Frontiers in neuroscience · 2017Article
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3 authors.
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Abstract
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 turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.
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