Comparing representations and computations in single neurons versus neural networks

Camilo Libedinsky

Abstract:

Single-neuron-level explanations have been the gold standard in neuroscience
for decades. Recently, however, neural-network-level explanations have be-
come increasingly popular. This increase in popularity is driven by the fact that
the analysis of neural networks can solve problems that cannot be addressed
by analyzing neurons independently. In this opinion article, I argue that while
both frameworks employ the same general logic to link physical and mental
phenomena, in many cases the neural network framework provides better
explanatory objects to understand representations and computations related
to mental phenomena. I discuss what constitutes a mechanistic explanation in
neural systems, provide examples, and conclude by highlighting a number of
the challenges and considerations associated with the use of analyses of neural
networks to study brain function.