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• W3037324086 abstract "How do humans learn from raw sensory experience? Throughout life, but most obviously in infancy, we learn without explicit instruction. We propose a detailed biological mechanism for the widely-embraced idea that learning is based on the differences between predictions and actual outcomes (i.e., predictive error-driven learning). Specifically, numerous weak projections into the pulvinar nucleus of the thalamus generate top-down predictions, and sparse, focal driver inputs from lower areas supply the actual outcome, originating in layer 5 intrinsic bursting (5IB) neurons. Thus, the outcome is only briefly activated, roughly every 100 msec (i.e., 10 Hz, alpha), resulting in a temporal difference error signal, which drives local synaptic changes throughout the neocortex, resulting in a biologically-plausible form of error backpropagation learning. We implemented these mechanisms in a large-scale model of the visual system, and found that the simulated inferotemporal (IT) pathway learns to systematically categorize 3D objects according to invariant shape properties, based solely on predictive learning from raw visual inputs. These categories match human judgments on the same stimuli, and are consistent with neural representations in IT cortex in primates." @default.
• W3037324086 created "2020-07-02" @default.
• W3037324086 creator A5012719980 @default.
• W3037324086 creator A5028735231 @default.
• W3037324086 creator A5045913278 @default.
• W3037324086 creator A5081344578 @default.
• W3037324086 date "2020-06-26" @default.
• W3037324086 modified "2023-09-27" @default.
• W3037324086 title "Deep Predictive Learning in Neocortex and Pulvinar" @default.
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• W3037324086 hasPublicationYear "2020" @default.
• W3037324086 type Work @default.

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