  Retinal image of surrounding objects varies tremendously due to the changes
in position, size, pose, illumination condition, background context, occlusion,
noise, and nonrigid deformations. But despite these huge variations, our visual
system is able to invariantly recognize any object in just a fraction of a
second. To date, various computational models have been proposed to mimic the
hierarchical processing of the ventral visual pathway, with limited success.
Here, we show that the association of both biologically inspired network
architecture and learning rule significantly improves the models' performance
when facing challenging invariant object recognition problems. Our model is an
asynchronous feedforward spiking neural network. When the network is presented
with natural images, the neurons in the entry layers detect edges, and the most
activated ones fire first, while neurons in higher layers are equipped with
spike timing-dependent plasticity. These neurons progressively become selective
to intermediate complexity visual features appropriate for object
categorization. The model is evaluated on 3D-Object and ETH-80 datasets which
are two benchmarks for invariant object recognition, and is shown to outperform
state-of-the-art models, including DeepConvNet and HMAX. This demonstrates its
ability to accurately recognize different instances of multiple object classes
even under various appearance conditions (different views, scales, tilts, and
backgrounds). Several statistical analysis techniques are used to show that our
model extracts class specific and highly informative features.
