A study by Massachusetts Institute of Technology (MIT) researchers revealed the difficulty of comparing neural networks to the brain.
The researchers studied over 11,000 neural networks trained to simulate the function of grid cells (part of the brain's navigation system) and found that very specific constraints not found in biological systems are needed for neural networks to generate grid-cell-like activity.
Researcher Rylan Schaeffer said, "What this suggests is that in order to obtain a result with grid cells, the researchers training the models needed to bake in those results with specific, biologically implausible implementation choices."
The analysis showed that close to 90% of the neural networks successfully learned path integration (a prediction of an animal's next location based on a given starting point and velocity), but grid-cell-like activity patterns were produced by just 10% of the networks.
From MIT News
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