No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit
Rylan Schaeffer, Mikail Khona, Ila Rani Fiete
Abstract
Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance based on deep learning. Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain.
The central claims of recent deep learning-based models of brain circuits are that they make novel predictions about neural phenomena or shed light on the fundamental functions being optimized. We show, through the case-study of grid cells in the entorhinal-hippocampal circuit, that one often gets neither.
We begin by reviewing the principles of grid cell mechanism and function obtained from analytical and first-principles modeling efforts, then rigorously examine the claims of deep learning models of grid cells. Using large-scale hyperparameter sweeps and theory-driven experimentation, we demonstrate that the results of such models may be more strongly driven by particular, non-fundamental, and post-hoc implementation choices than fundamental truths about neural circuits or the loss function(s) they might optimize.
Finally, we discuss why these models cannot be expected to produce accurate models of the brain without the addition of substantial amounts of inductive bias, an informal No Free Lunch result for Neuroscience. In conclusion, caution and consideration, together with biological knowledge, are warranted in building and interpreting deep learning models in Neuroscience.
Full = pdf https://t.co/XBjH1m8X1E
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placed in Research methodology because primary relevance is the caution needed to be attached to the Deep Learning tag. Full article is extremely technical.
Rylan Schaeffer, Mikail Khona, Ila Rani Fiete
Abstract
Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance based on deep learning. Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain.
The central claims of recent deep learning-based models of brain circuits are that they make novel predictions about neural phenomena or shed light on the fundamental functions being optimized. We show, through the case-study of grid cells in the entorhinal-hippocampal circuit, that one often gets neither.
We begin by reviewing the principles of grid cell mechanism and function obtained from analytical and first-principles modeling efforts, then rigorously examine the claims of deep learning models of grid cells. Using large-scale hyperparameter sweeps and theory-driven experimentation, we demonstrate that the results of such models may be more strongly driven by particular, non-fundamental, and post-hoc implementation choices than fundamental truths about neural circuits or the loss function(s) they might optimize.
Finally, we discuss why these models cannot be expected to produce accurate models of the brain without the addition of substantial amounts of inductive bias, an informal No Free Lunch result for Neuroscience. In conclusion, caution and consideration, together with biological knowledge, are warranted in building and interpreting deep learning models in Neuroscience.
Full = pdf https://t.co/XBjH1m8X1E
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placed in Research methodology because primary relevance is the caution needed to be attached to the Deep Learning tag. Full article is extremely technical.
