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The construction of the world's largest functional brain model and large-scale, real-time hardware simulations rely on the same mathematical and neuromorphic methods.
In this paper we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. This allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex.
Co-developing neuromorphic integrated circuit learning models and derivations significantly increases biological accuracy and reduces circuit complexity.
A mathematical framework is being used to investigate the learning dynamics induced by a class of biologically realistic synaptic plasticity rules in recurrently connected neuronal networks.
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