Abstract
Learning and adaptation are central to the design of neuromorphic VLSI systems that perform robustly in variable and unpredictable environments.
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Cauwenberghs, G. (1998). Analog VLSI Stochastic Perturbative Learning Architectures. In: Lande, T.S. (eds) Neuromorphic Systems Engineering. The Springer International Series in Engineering and Computer Science, vol 447. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-28001-1_18
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DOI: https://doi.org/10.1007/978-0-585-28001-1_18
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