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Synaptic homeostasis is a mechanism present in biological neural systems that acts to maintain an homogeneous and stable computational substrate, in face of intrinsic inhomogeneities among neurons, and of their continuous changes due to learning processes and variations in the statistics of the input signals. In hardware spike-based neural networks homeostasis could be useful for solving issues such as mismatch and temperature drifts. Here we present a synaptic circuit that supports both spike-based learning and homeostatic mechanisms, and show how it can be used in conjunction with a software control algorithm to model global synaptic scaling homeostatic mechanism.
|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||07 Faculty of Science > Institute of Neuroinformatics|
|DDC:||570 Life sciences; biology|
|Uncontrolled Keywords:||aVLSI; Neuromorphic; Synapse; Homeostasis; Spike-based learning; Synaptic scaling|
|Deposited On:||28 Feb 2010 10:44|
|Last Modified:||23 Nov 2012 13:02|
|Free access at:||Related URL. An embargo period may apply.|
|Related URLs:||http://www.ini.uzh.ch/~giacomo/papers/pdf/neurocomputing09.pdf (Organisation)|
Scopus®. Citation Count: 10
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