| Paper: | PS-2B.70 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Do Biologically-Realistic Recurrent Architectures Produce Biologically-Realistic Models? | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1175-0 | ||
| Authors: | Grace Lindsay, Theodore Moskovitz, Guangyu Robert Yang, Kenneth Miller, Columbia University, United States | ||
| Abstract: | Many details are known about microcircuitry in visual cortices. For example, neurons have supralinear activation functions, they're either excitatory (E) or inhibitory (I), connection strengths fall off with distance, and the output cells of an area are excitatory. This circuitry is important as it's believed to support core functions such as normalization and surround suppression. Yet, multi-area models of the visual processing stream don't usually include these details. Here, we introduce known-features of recurrent processing into the architecture of a convolutional neural network and observe how connectivity and activity change as a result. We find that certain E-I differences found in data emerge in the models, though the details depend on which architectural elements are included. We also compare the representations learned by these models to data, and perform analyses on the learned weight structures to assess the nature of the neural interactions. | ||