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Distributed Representations Enable Robust Multi-Timescale Computation in Neuromorphic Hardware
Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge.
Vector Symbolic Finite State Machines in Attractor Neural Networks
Hopfield attractor networks are robust distributed models of human memory, but lack a general mechanism for effecting state-dependent attractor transitions in response to input.
Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons
Lethal Autonomous Weapons promise to revolutionize warfare -- and raise a multitude of ethical and legal questions.
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