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Partially inspired by features of computation in visual cortex, deep neuralnetworks compute hierarchical representations of their inputs. While thesenetworks have been highly successful in machine learning, it remains unclear towhat extent they can aid our understanding of cortical function. Several groupshave developed metrics that provide a quantitative comparison betweenrepresentations computed by networks and representations measured in cortex. Atthe same time, neuroscience is well into an unprecedented phase of large-scaledata collection, as evidenced by projects such as the Allen Brain Observatory.Despite the magnitude of these efforts, in a given experiment only a fractionof units are recorded, limiting the information available about the corticalrepresentation. Moreover, only a finite number of stimuli can be shown to ananimal over the course of a realistic experiment. These limitations raise thequestion of how and whether metrics that compare representations of deepnetworks are meaningful on these datasets. Here, we empirically quantify thecapabilities and limitations of these metrics due to limited imagepresentations and neuron samples. We find that the comparison procedure isrobust to different choices of stimuli set and the level of subsampling thatone might expect in a large-scale brain survey with thousands of neurons. Usingthese results, we compare the representations measured in the Allen BrainObservatory in response to natural image presentations to deep neural network.We show that the visual cortical areas are relatively high orderrepresentations (in that they map to deeper layers of convolutional neuralnetworks). Furthermore, we see evidence of a broad, more parallel organizationrather than a sequential hierarchy, with the primary area VISp(V1) being lowerorder relative to the other areas.

Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual Cortex