Abstract
We present a recurrent learning system that can incrementally integrate stimuli in two modalities, visual and auditory. The system consists of five self-organizing modules, each mapping input stimuli into respective latent spaces. Two sensory modules convert the input stimuli into an internal 3-D “neuronal code”. The central module integrates the bimodal information, and through modulatory top-down feedback influences the organization of data in two unimodal association units. Two feedback gains control the strength of the feedback connection. As an example we selected a set of Chinese characters and related spoken words. It is shown that the learning system can build a stable neuronal structure for incrementally applied visual and auditory stimuli.
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Papliński, A.P., Mount, W.M. (2013). Bimodal Incremental Self-Organizing Network (BiSON) with Application to Learning Chinese Characters. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_16
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DOI: https://doi.org/10.1007/978-3-642-42054-2_16
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