Probing Biological and Artificial Neural Networks with Task-dependent
  Neural Manifolds
Michael Kuoch Chi-Ning Chou Nikhil Parthasarathy Joel Dapello James J. DiCarlo Haim Sompolinsky SueYeon Chung
Recently, growth in our understanding of the computations performed in both
biological and artificial neural networks has largely been driven by either
low-level mechanistic studies or global normative approaches. However, concrete
methodologies for bridging the gap between these levels of abstraction remain
elusive. In this work, we investigate the internal mechanisms of neural
networks through the lens of neural population geometry, aiming to provide
understanding at an intermediate level of abstraction, as a way to bridge that
gap. Utilizing manifold capacity theory (MCT) from statistical physics and
manifold alignment analysis (MAA) from high-dimensional statistics, we probe
the underlying organization of task-dependent manifolds in deep neural networks
and macaque neural recordings. Specifically, we quantitatively characterize how
different learning objectives lead to differences in the organizational
strategies of these models and demonstrate how these geometric analyses are
connected to the decodability of task-relevant information. These analyses
present a strong direction for bridging mechanistic and normative theories in
neural networks through neural population geometry, potentially opening up many
future research avenues in both machine learning and neuroscience.