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Biologically plausible deep learning—But how far can we go with shallow networks?
یادگیری عمیق زیست شناختی قابل قبول: اما تا چه حد می توانیم با شبکه های کم عمق برویم؟-2019 Training deep neural networks with the error backpropagation algorithm is considered implausible
from a biological perspective. Numerous recent publications suggest elaborate models for biologically
plausible variants of deep learning, typically defining success as reaching around 98% test accuracy on
the MNIST data set. Here, we investigate how far we can go on digit (MNIST) and object (CIFAR10)
classification with biologically plausible, local learning rules in a network with one hidden layer and
a single readout layer. The hidden layer weights are either fixed (random or random Gabor filters)
or trained with unsupervised methods (Principal/Independent Component Analysis or Sparse Coding)
that can be implemented by local learning rules. The readout layer is trained with a supervised, local
learning rule. We first implement these models with rate neurons. This comparison reveals, first, that
unsupervised learning does not lead to better performance than fixed random projections or Gabor
filters for large hidden layers. Second, networks with localized receptive fields perform significantly
better than networks with all-to-all connectivity and can reach backpropagation performance on
MNIST. We then implement two of the networks – fixed, localized, random & random Gabor filters in
the hidden layer – with spiking leaky integrate-and-fire neurons and spike timing dependent plasticity
to train the readout layer. These spiking models achieve >98.2% test accuracy on MNIST, which is
close to the performance of rate networks with one hidden layer trained with backpropagation. The
performance of our shallow network models is comparable to most current biologically plausible
models of deep learning. Furthermore, our results with a shallow spiking network provide an important
reference and suggest the use of data sets other than MNIST for testing the performance of future
models of biologically plausible deep learning. Keywords: Deep learning | Local learning rules | Random projections | Unsupervised feature learning | Spiking networks | MNIST |
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