Neuromimetic Circuits with Synaptic Devices Based on Strongly Correlated Electron Systems

A crucial feature of biological neural architectures is their ability to learn, and unlearn, in response to external stimulation. In this work the authors reproduce this feature in an electronic network composed of strongly correlated electron materials implemented as synaptic devices. This network responds to both excitatory and inhibitory excitations, exhibits associative as well as nonassociative learning, and even displays habituation-like behavior and other aspects of authentic neuronal systems. This opens avenues for both investigating biological behaviors and designing computers with the capacity to learn and remember based on hardware alone.


See on Scoop.itBiobit: Computational Neuroscience & Biocomputation


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