However, looking at deep neural networks, which have achieved remarkable results in tasks ranging from cancer detection to self-driving cars, may provide useful insights. We do not even know if such a brain theory should be at the molecular level or at the level of brain regions, or at any scale between. It has a myriad of detailed observations but no single theory explaining the connections between all of those observations. Neuroscience is at the stage biology was at before Darwin. Thus, the ability of a single neuron to minimize surprise-that is, the difference between actual and expected activity-could be an important missing element to understand computation in the brain. Our results also suggest that spontaneous brain activity provides ‘training data’ for neurons to learn to predict cortical dynamics. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithm, thus offering a step towards the development of a general theory of neuronal learning. Interestingly, this predictive learning rule can be derived from a metabolic principle, whereby neurons need to minimize their own synaptic activity (cost) while maximizing their impact on local blood supply by recruiting other neurons. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. However, it is still not well understood how a predictive system could be implemented in the brain. It was previously proposed that the brain may operate on the principle of predictive coding. Understanding how the brain learns may lead to machines with human-like intellectual capacities.
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