  Computational models of vision have traditionally been developed in a
bottom-up fashion, by hierarchically composing a series of straightforward
operations - i.e. convolution and pooling - with the aim of emulating simple
and complex cells in the visual cortex, resulting in the introduction of deep
convolutional neural networks (CNNs). Nevertheless, data obtained with recent
neuronal recording techniques support that the nature of the computations
carried out in the ventral visual stream is not completely captured by current
deep CNN models. To fill the gap between the ventral visual stream and deep
models, several benchmarks have been designed and organized into the
Brain-Score platform, granting a way to perform multi-layer (V1, V2, V4, IT)
and behavioral comparisons between the two counterparts. In our work, we aim to
shift the focus on architectures that take into account lateral recurrent
connections, a ubiquitous feature of the ventral visual stream, to devise
adaptive receptive fields. Through recurrent connections, the input s
long-range spatial dependencies can be captured in a local multi-step fashion
and, as introduced with Gated Recurrent CNNs (GRCNN), the unbounded expansion
of the neuron s receptive fields can be modulated through the use of gates. In
order to increase the robustness of our approach and the biological fidelity of
the activations, we employ specific data augmentation techniques in line with
several of the scoring benchmarks. Enforcing some form of invariance, through
heuristics, was found to be beneficial for better neural predictivity.
