Abstract
Stacy et.al. demonstrated that an array of simulated hippocampal CA1 neurons exhibited stochastic resonance-like behaviors where an optimal correlation value between the sub-threshold input and the output was obtained by tuning both the noise intensity and the connection strength between the CA1 neurons. Based on this model, we proposed a simple neural network model for semiconductor devices. We carried out simulations using internal noise sources and confirmed that the correlation value between input and output in the network increased as the coupling strength increased.
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Tovar, G.M., Asai, T., Amemiya, Y. (2010). Array-Enhanced Stochastic Resonance in a Network of Noisy Neuromorphic Circuits. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_24
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DOI: https://doi.org/10.1007/978-3-642-17537-4_24
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