
Which Artificial Neural Network for Object Recognition Is Most Brain-Like? - Schiphol
https://www.biorxiv.org/content/10.1101/407007v2.full
======
Schiphol
Abstract: The internal representations of early deep artificial neural
networks (ANNs) were found to be remarkably similar to the internal neural
representations measured experimentally in the primate brain. Here we ask, as
deep ANNs have continued to evolve, are they becoming more or less brain-like?
ANNs that are most functionally similar to the brain will contain mechanisms
that are most like those used by the brain. We therefore developed Brain-Score
– a composite of multiple neural and behavioral benchmarks that score any ANN
on how similar it is to the brain’s mechanisms for core object recognition –
and we deployed it to evaluate a wide range of state-of-the-art deep ANNs.
Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and
ResNet-101 are the most brain-like ANNs. (2) There remains considerable
variability in neural and behavioral responses that is not predicted by any
ANN, suggesting that no ANN model has yet captured all the relevant
mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet
performance led to gains on Brain-Score. However, correlation weakened at ≥
70% top-1 ImageNet performance, suggesting that additional guidance from
neuroscience is needed to make further advances in capturing brain mechanisms.
(4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like
than many of the best-performing ImageNet models, which suggests the
opportunity to simplify ANNs to better understand the ventral stream. The
scoring system used here is far from complete. However, we propose that
evaluating and tracking model-benchmark correspondences through a Brain-Score
that is regularly updated with new brain data is an exciting opportunity:
experimental benchmarks can be used to guide machine network evolution, and
machine networks are mechanistic hypotheses of the brain’s network and thus
drive next experiments. To facilitate both of these, we release Brain-
Score.org: a platform that hosts the neural and behavioral benchmarks, where
ANNs for visual processing can be submitted to receive a Brain-Score and their
rank relative to other models, and where new experimental data can be
naturally incorporated.

Direct link to pdf:
[https://www.biorxiv.org/content/10.1101/407007v2.full.pdf](https://www.biorxiv.org/content/10.1101/407007v2.full.pdf)

