Abstract
Computational tasks related to processing and recognition of natural signal require identification of complex patterns and relationships in massive quantities of low precision, ambiguous noisy data. While state-of-the-art techniques and architectures fail to provide sufficient solutions, cortical neural networks have an inherent computational power in this domain. A recently-introduced Liquid-State-Machine (LSM) paradigm provides a computational framework for applying a model of cortical neural microcircuit as a core computational unit in classification and recognition tasks of real-time temporal data. In this study we apply the concept of “Neural Cliques” and extend the computational power of the LSM framework by closing the loop. By incorporating functions of readout, reward and feedback, we implement such a closed-loop framework of neural architecture in classification and recognition tasks of real-time temporal data. This approach is inspired by several neurobiological findings from ex-vivo multi-cellular electrical recordings and injection of dopamine to the neural culture. Finally, we illustrate the performance of the proposed architecture in word-recognition tasks.
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Odinaev, K., Raichelgauz, I., Zeevi, Y.Y. (2006). Mapping of Natural Patterns by Liquid Architectures Implementing Neural Cliques. In: Tiwari, A., Roy, R., Knowles, J., Avineri, E., Dahal, K. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36266-1_12
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DOI: https://doi.org/10.1007/978-3-540-36266-1_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29123-7
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