Neurons learn by predicting future activity.
Neurons learn by predicting future activity.
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DOI：
10.1038/s42256-021-00430-y
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发表时间：
2022-01
影响因子：
23.8
通讯作者：
Kubo, Yoshimasa
中科院分区：
文献类型：
--
作者：
Luczak, Artur;McNaughton, Bruce L.;Kubo, Yoshimasa
了解大脑的学习方式可能会让机器拥有类似人类的智力。此前有人提出，大脑可能是根据预测编码原理运作的。然而，预测系统是如何在大脑中实现的，目前还没有得到很好的理解。在这里，我们证明了单个神经元预测其未来活动的能力可能提供一种有效的学习机制。有趣的是，这种预测学习规则可以来源于代谢原理，即神经元需要最小化自己的突触活动（成本），同时通过招募其他神经元来最大化它们对局部血液供应的影响。我们展示了这种数学推导的学习规则如何在不同类型的大脑启发算法之间提供理论联系，从而为神经元学习的一般理论的发展提供了一步。我们在神经网络模拟和清醒动物的数据记录中测试了这种预测学习规则。我们的研究结果还表明，自发的大脑活动为神经元学习预测皮层动态提供了“训练数据”。因此，单个神经元最小化意外的能力，即实际活动和预期活动之间的差异，可能是理解大脑计算的重要缺失元素。
Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, thus, offering a step toward development of a general theory of neuronal learning. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, the ability of a single neuron to minimize surprise: i.e. the difference between actual and expected activity, could be an important missing element to understand computation in the brain.
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DOI：
10.1073/pnas.89.10.4363
发表时间：
1992-05-15
影响因子：
11.1
作者：
DUDEK, SM;BEAR, MF
通讯作者：
BEAR, MF
影响因子：
16.2
作者：
Bastos AM;Usrey WM;Adams RA;Mangun GR;Fries P;Friston KJ
通讯作者：
Friston KJ
影响因子：
16.2
作者：
Gomez, M;De Castro, E;Nef, P
通讯作者：
Nef, P
影响因子：
5.5
作者：
ECCLES, JC;FATT, P;KOKETSU, K
通讯作者：
KOKETSU, K
影响因子：
5.5
作者：
GUTFREUND, Y;YAROM, Y;SEGEV, I
通讯作者：
SEGEV, I