  Brain-inspired machine learning is gaining increasing consideration,
particularly in computer vision. Several studies investigated the inclusion of
top-down feedback connections in convolutional networks; however, it remains
unclear how and when these connections are functionally helpful. Here we
address this question in the context of object recognition under noisy
conditions. We consider deep convolutional networks (CNNs) as models of
feed-forward visual processing and implement Predictive Coding (PC) dynamics
through feedback connections (predictive feedback) trained for reconstruction
or classification of clean images. To directly assess the computational role of
predictive feedback in various experimental situations, we optimize and
interpret the hyper-parameters controlling the network's recurrent dynamics.
That is, we let the optimization process determine whether top-down connections
and predictive coding dynamics are functionally beneficial. Across different
model depths and architectures (3-layer CNN, ResNet18, and EfficientNetB0) and
against various types of noise (CIFAR100-C), we find that the network
increasingly relies on top-down predictions as the noise level increases; in
deeper networks, this effect is most prominent at lower layers. In addition,
the accuracy of the network implementing PC dynamics significantly increases
over time-steps, compared to its equivalent forward network. All in all, our
results provide novel insights relevant to Neuroscience by confirming the
computational role of feedback connections in sensory systems, and to Machine
Learning by revealing how these can improve the robustness of current vision
models.
