Abstract
Neural models, artificial or biologically grounded, have been used for understanding the nature of learning mechanisms as well as for applied tasks. The study of such learning systems has been typically centered on the identification or extraction of the most relevant features that will help to solve a task. Recently, convolutional networks, deep architectures and huge reservoirs have shown impressive results in tasks ranging from speech recognition to visual classification or emotion perception. With the accumulated momentum of such large-scale architectures, the importance of imposing sparsity on the networks to differentiate contexts has been rising. We present a biologically grounded system that imposes physical and local constraints to these architectures in the form of synaptogenesis, or synapse generation. This method guarantees sparsity and promotes the acquisition of experience-relevant, topologically-organized and more diverse features.
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Puigbò, JY., van Wijngaarden, J., Low, S.C., Verschure, P.F.M.J. (2016). Synaptogenesis: Constraining Synaptic Plasticity Based on a Distance Rule. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_4
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DOI: https://doi.org/10.1007/978-3-319-44778-0_4
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