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https://hdl.handle.net/2440/81679
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Type: | Journal article |
Title: | Spike-timing-dependent construction |
Author: | Lightheart, T. Grainger, S. Lu, T. |
Citation: | Neural Computation, 2013; 25(10):2611-2645 |
Publisher: | M I T Press |
Issue Date: | 2013 |
ISSN: | 0899-7667 1530-888X |
Statement of Responsibility: | Toby Lightheart, Steven Grainger, and Tien-Fu Lu |
Abstract: | 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. |
Keywords: | Neural Pathways Neurons Excitatory Postsynaptic Potentials Neuronal Plasticity Algorithms Models, Neurological Artificial Intelligence Computer Simulation Image Processing, Computer-Assisted Neural Networks, Computer |
Rights: | © 2013 Massachusetts Institute of Technology |
DOI: | 10.1162/NECO_a_00501 |
Published version: | http://dx.doi.org/10.1162/neco_a_00501 |
Appears in Collections: | Aurora harvest Mechanical Engineering publications |
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