Bridging the gap between Deep Learning and Neuroscience
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2018-10-31
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en
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Abstract
In recent years, Deep Learning has achieved superhuman abilities in many tasks such as
visual object recognition. Nevertheless, the brain outperforms Deep Networks in its ability
to generalize to distorted images. Yet the exact mechanisms used to achieve this invariance
are still not understood completely. The interplay between neuroscience and Deep Learning
could both advance the knowledge on the processes that occur in the brain and help the
development of more efficient artificial networks. The aim of the present paper is to study
the link between the brain and artificial neural models by comparing the behavior of a
Convolutional Neural Network to our knowledge of the processing of visual information in
the human cortex. The network’s recognition ability under invariance conditions was tested
when presenting input images that were different from the images employed for the training
of the network. The test images were modified either with geometric deformations, by
varying the rotation, position and size of the objects within the image, or by compromising
the extent of visual information transmitted from the input when changing the quality,
contrast and amount of noise. The results are compared to neural data obtained from
behavioral and neuroimaging studies in which the subject’s response time, accuracy and
neural activations were recorded following the presentation of images with the various types
of deformations. Furthermore, the fundamental characteristics of the architecture of the
network and the backpropagation algorithm used for the training process are discussed
in comparison to the structure of the visual stream and to the synaptic update processes
that are thought to be employed by the brain for learning. Our investigation highlights
that a great issue with current Deep Neural Networks is the limited performance under
image distortions as compared to humans’ invariant recognition ability. Furthermore, the
present study underlines the differences in the implementation of the learning algorithm
in computational models and in the brain as a starting point to improve Deep Learning
towards more efficient and more biologically plausible networks.
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Faculteit der Sociale Wetenschappen