2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: GS-1.1
Session: Contributed Talks 1-2
Location: H0104
Session Time: Saturday, September 14, 09:50 - 10:30
Presentation Time:Saturday, September 14, 09:50 - 10:10
Presentation: Oral
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Learning Divisive Normalization in Primary Visual Cortex
Manuscript:  Click here to view manuscript
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License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1211-0
Authors: Max F. Günthner, Santiago A. Cadena, University of Tübingen, Germany; George H. Denfield, Edgar Y. Walker, Baylor College of Medicine, United States; Leon A. Gatys, University of Tübingen, Germany; Andreas S. Tolias, Baylor College of Medicine, United States; Matthias Bethge, Alexander S. Ecker, University of Tübingen, Germany
Abstract: Divisive normalization (DN) has been suggested as a canonical computation implemented throughout the neocortex. In primary visual cortex (V1), DN was found to be crucial to explain nonlinear response properties of neurons when presented with superpositions of simple stimuli such as gratings. Based on such studies, it is currently assumed that neuronal responses to stimuli restricted to the neuron's classical receptive field (RF) are normalized by a non-specific pool of nearby neurons with similar RF locations. However, it is currently unknown how DN operates in V1 when processing natural inputs. Here, we investigated DN in monkey V1 under stimulation with natural images with an end-to-end trainable model that learns the pool of normalizing neurons and the magnitude of their contribution directly from the data. Taking advantage of our model's direct interpretable view of V1 computation, we found that oriented features were normalized preferentially by features with similar orientation preference rather than non-specifically. Our model's accuracy was competitive with state-of-the-art black-box models, suggesting that rectification, DN, and a combination of subunits resulting from DN are sufficient to account for V1 responses to localized stimuli. Thus, our work significantly advances our understanding of V1 function.