Instance-Based Learning Algorithms
Machine Learning
Dynamic cell structure learns perfectly topology preserving map
Neural Computation
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Systems and Computers in Japan
Polychronization: Computation with Spikes
Neural Computation
Neurons Tune to the Earliest Spikes Through STDP
Neural Computation
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Evolution of spiking neural circuits in autonomous mobile robots: Research Articles
International Journal of Intelligent Systems - Intentional Dynamic Systems—Foundations, Modeling, and Robot Implementation
A fast learning algorithm for deep belief nets
Neural Computation
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lower bounds for the computational power of networks of spiking neurons
Neural Computation
Phenomenological models of synaptic plasticity based on spike timing
Biological Cybernetics - Special Issue: Object Localization
Competitive stdp-based spike pattern learning
Neural Computation
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning anticipation via spiking networks: application to navigation control
IEEE Transactions on Neural Networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
IEEE Transactions on Neural Networks
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
IEEE Transactions on Neural Networks
Simple model of spiking neurons
IEEE Transactions on Neural Networks
An efficient learning procedure for deep boltzmann machines
Neural Computation
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Spike-timing-dependent construction STDC is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity STDP, significant effort has gone into the modeling and simulation of adaptation in spiking neural networks SNNs. Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for applications of unknown complexity and nonstationary solutions. A conceptual analogy is developed and extended to theoretical conditions for modeling synaptic plasticity as network construction. Generalizing past constructive algorithms, we propose a framework for the design of novel constructive SNNs and demonstrate its application in the development of simulations for the validation of developed theory. Potential directions of future research and applications of STDC for biological modeling and machine learning are also discussed.