  Finding a code to unravel the population of neural responses that leads to a
distinct animal behavior has been a long-standing question in the field of
neuroscience. With the recent advances in machine learning, it is shown that
the hierarchically Deep Neural Networks (DNNs) perform optimally in decoding
unique features out of complex datasets. In this study, we utilize the power of
a DNN to explore the computational principles in the mammalian brain by
exploiting the Neuropixel data from Allen Brain Institute. We decode the neural
responses from mouse visual cortex to predict the presented stimuli to the
animal for natural (bear, trees, cheetah, etc.) and artificial (drifted
gratings, orientated bars, etc.) classes. Our results indicate that neurons in
mouse visual cortex encode the features of natural and artificial objects in a
distinct manner, and such neural code is consistent across animals. We
investigate this by applying transfer learning to train a DNN on the neural
responses of a single animal and test its generalized performance across
multiple animals. Within a single animal, DNN is able to decode the neural
responses with as much as 100% classification accuracy. Across animals, this
accuracy is reduced to 91%. This study demonstrates the potential of utilizing
the DNN models as a computational framework to understand the neural coding
principles in the mammalian brain.
