@article{56,
  keywords = {evolution, experimental design, interpolation, learning, neural networks},
  author = {Uri Hasson and Samuel Nastase and Ariel Goldstein},
  title = {Robust Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.},
  abstract = {<p>Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have exposed the power of optimizing millions of synaptic weights over millions of observations to operate robustly in real-world contexts. These models do not learn simple, human-interpretable rules or representations of the world; rather, they use local computations to interpolate over task-relevant manifolds in a high-dimensional parameter space. Counterintuitively, similar to evolutionary processes, over-parameterized models can be simple and parsimonious, as they provide a versatile, robust solution for learning a diverse set of functions. This new family of direct-fit models present a radical challenge to many of the theoretical assumptions in psychology and neuroscience. At the same time, this shift in perspective establishes unexpected links with developmental and ecological psychology.</p>
},
  year = {2020},
  journal = {Neuron},
  volume = {105},
  pages = {416-434},
  issn = {1097-4199},
  doi = {10.1016/j.neuron.2019.12.002},
  language = {eng},
}