Abstract
We previously proposed a neural segmentation model suitable for implementation with complementary metal-oxide-semiconductor (CMOS) circuits. The model consists of neural oscillators mutually coupled through synaptic connections. The learning is governed by a symmetric spike-timing-dependent plasticity (STDP). Here we demonstrate and evaluate the circuit operation of the proposed model with a network consisting of six oscillators. Moreover, we explore the effects of mismatch in the threshold voltage of transistors, and demonstrate that the network was tolerant to mismatch (noise).
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Tovar, G.M., Asai, T., Amemiya, Y. (2009). Noise-Tolerant Analog Circuits for Sensory Segmentation Based on Symmetric STDP Learning. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_104
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DOI: https://doi.org/10.1007/978-3-642-03040-6_104
Publisher Name: Springer, Berlin, Heidelberg
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