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Inspired by my experiences in modeling adult hippocampal neurogenesis, in this talk I will describe how the challenges of modeling the diverse time and spatial scales of the brain’s plasticity and dynamics led me to look towards the emerging field of neuromorphic computing as a potential solution. Today’s neuromorphic systems are beginning to reach brain-like scales in terms of numbers of neurons. I will describe a range of unexpected results that we have learned from brain-like computing systems operating at this scale on a wide range of cognitive and non-cognitive tasks. In addition to providing a tool to perform neural simulations, neuromorphic hardware may enable energy-efficient artificial intelligence. Moreover, neuromorphic approaches offer a path towards more advanced real-time computing capabilities for biomedical applications with strict performance, efficiency, and security demands. Finally, I will revisit my original goal of how understanding neural computations at biologically realistic scales on neuromorphic systems can, in turn, help us understand how and why the brain operates the way that it does.
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