The state-of-the art machine learning approach to training deep neural networks, backpropagation, is implausible for real neural networks: neurons need to know their outgoing weights; training alternates between a  forward pass (computation) and a  backward pass (learning); and the algorithm needs a large amount of labeled data . Biologically plausible approximations to backpropagation, such as feedback alignment, solve the weight transport problem, but not the other two. Thus, fully biologically plausible learning rules have so far remained elusive. Here we present a family of learning rules that does not suffer from any of these problems. It is motivated by the information bottleneck principle (extended with kernel methods), in which networks learn to squeeze as much information as possible out of the input without sacrificing prediction of the output. The resulting rules have a 3-factor Hebbian structure: they require pre- and post-synaptic firing rates and a global error signal - the third factor - that can be supplied by a neuromodulator. Moreover, they do not require precise labels; instead, they rely on the similarity between the desired outputs. They thus solve all three implausibility issues of backpropagation. Moreover, to obtain good performance on hard problems and retain biologically plausible learning rules , our rules need divisive normalization - a known feature of biological networks. Finally, simulations show that our rule performs nearly as well as backpropagation on image classification tasks.
The state-of-the art machine learning approach to training deep neural networks, backpropagation, is implausible for real neural networks: neurons need to know their outgoing weights; training alternates between a bottom-up forward pass (computation) and a top-down backward pass (learning); and the algorithm often needs precise labels of many data points . Biologically plausible approximations to backpropagation, such as feedback alignment, solve the weight transport problem, but not the other two. Thus, fully biologically plausible learning rules have so far remained elusive. Here we present a family of learning rules that does not suffer from any of these problems. It is motivated by the information bottleneck principle (extended with kernel methods), in which networks learn to compress the input as much as possible without sacrificing prediction of the output. The resulting rules have a 3-factor Hebbian structure: they require pre- and post-synaptic firing rates and an error signal - the third factor - consisting of a global teaching signal and a layer-specific term, both available without a top-down pass. They do not require precise labels; instead, they rely on the similarity between pairs of desired outputs. Moreover, to obtain good performance on hard problems and retain biological plausibility , our rules need divisive normalization - a known feature of biological networks. Finally, simulations show that our rules perform nearly as well as backpropagation on image classification tasks.
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bottom-up
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top-down
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needs a large amount of labeled data
often needs precise labels of many data points
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squeeze as much information as possible out of the input
compress the input as much as possible
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a global
an
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that can be supplied by a neuromodulator. Moreover, they
consisting of a global teaching signal and a layer-specific term, both available without a top-down pass. They
R
the
pairs of
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They thus solve all three implausibility issues of backpropagation.
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biologically plausible learning rules
biological plausibility
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rule performs
rules perform