| Paper: | PS-2B.6 |
| Session: | Poster Session 2B |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Reverse Engineering Neural Networks From Many Partial Recordings |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1037-0 |
| Authors: |
Elahe Arani, Radboud University Nijmegen, Netherlands; Sofia Triantafillou, Konrad Kording, University of Pennsylvania, United States |
| Abstract: |
Much of neuroscience aims at reconstructing brain function, but we only record a small number of neurons at a time. We do not currently know if simultaneous recording of most neurons is required for successful reconstruction, or if multiple recordings from smaller subsets suffice. This is made even more important as novel techniques allow recording from selected subsets of neurons. To get at this question, we analyze a neural network, trained on the MNIST dataset, using only partial recordings and characterize the dependency of the quality of our reverse engineering on the number of simultaneously recorded "neurons". We find that prediction in the nonlinear neural network is meaningfully possible if a sufficiently large number of neurons is simultaneously recorded but that this number can be considerably smaller than the number of neurons. Moreover, recording many times from small random subsets of neurons yields surprisingly good performance. This type of analysis we perform here can be used to calibrate approaches that can dramatically scale up the size of recorded data sets in neuroscience. |
