Using neuroscience to develop artificial intelligence

Ullman, Shimon·2019·Science
DOI 10.1126/science.aau6595

Cited by (231)

Items 101–200 of 231

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  45. Robotic Implementation of Brain-Inspired Multi-Sensory Integration and Decision Making
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  46. A Nonsynaptic Memory Based Neural Network for Hand-Written Digit Classification Using an Explainable Feature Extraction Method
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  47. Adaptive Memory and In Materia Reinforcement Learning Enabled by Flexoelectric-like Response from Ultrathin HfO2
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  48. Why Social Robots Need Self-Preservation to Be Objects of Moral Consideration
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  50. Effect of thickness and halide composition on the resistive switching and photonic synapse properties of methylammonium lead bromide thin films
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  51. An Investigation into the Relationship between Curse of Dimensionality and Dunning-Kruger Effect
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  53. Toward One-Shot Learning in Neuroscience-Inspired Deep Spiking Neural Networks
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  55. Neuromorphic Computing: Bridging the Gap Between Neuroscience and Artificial Intelligence
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  58. Perspectives on halide perovskite-based in-memory and neuromorphic computing devices
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  87. Computational Pipeline to probe NaV1.7 gain-of-functions variants in neuropathic painful syndromes
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  90. Applying Principles from Medicine Back to Artificial Intelligence
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  91. Developments in Computer Science and Technical Applications
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  92. New Results from Brain Research and Neuroscience
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  93. References
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  97. Brinkmanship in algorithmic warfare
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  98. Thinking forward with counterfactuals
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  99. Process tracing
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  100. Conclusion
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