comparison against task driven artificial neural networks ... · coding throughout the mouse visual...

We wish to thank the Allen Institute for Brain Science founder, Paul G. Allen, for his vision, encouragement, and support. brainscience.alleninstitute.org brain-map.org • VGG16-pseudo-depth • Yardstick for studying representations • Absolute values of the metrics are sensitive to sub-sampling neurons • O(10 3 ) neurons needed for robust comparison Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual Cortex Jianghong Shi 1 , Eric Shea-Brown 1,2 , Michael A. Buice 1,2 1 University of Washington, 2 Allen Institute for Brain Science. • The Allen Brain Observatory presents the first standardized in vivo survey of physiological activity in the mouse visual cortex, featuring representations of visually evoked calcium responses from GCaMP6-expressing neurons in selected cortical layers, visual areas and Cre lines • We use data from six areas of visual cortex (VISp, VISl, VISal, VISpm, VISam, VISrl) under natural scene stimuli Allen Brain Observatory Enables Large Population Coding Schematic of Approach • Comparing representation matrices require proper metrics Metric 1: Similarity of Similarity Matrices (SSM) • PCC: Pearson correlation coefficient • SRC: Spearman rank coefficient Metric 2: Singular Value Canonical Correlation Analysis (SVCCA) • Reduce neuron dimension by PCA • Take mean of top canonical correlation coefficients Metrics Reveal Functional Organization of Mouse Visual Cortex Metrics Robustly Differentiate Deep Neural Network Layers References [1.] de Vries, Saskia E J et al., A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex, bioRxiv (2018) [2.] Yamins, Daniel L. K. et al., Performance- Optimized Hierarchical Models Predict Neural Responses in Higher Visual Cortex, PNAS (2014) [3.] Kriegeskorte, Nikolaus et al., Representational Similarity Analysis - Connecting the Branches of Systems Neuroscience, Front Syst Neurosci. (2008) [4.] Raghu, Maithra et al., SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability, NeurIPS (2017) Acknowledgement We acknowledge the NIH Graduate training grant in neural computation and engineering (2R90DA033461). VGG16 layers vs. VGG16 layers with different image sets Sub-sampled VGG19 layers vs. VGG16 layers Mouse visual areas vs. VGG16 layers Specific cortical layer and area vs. VGG16 layers • The emerging large scale neural data sets usually have limited testing conditions and partially observed populations of neurons • Deep nets reveal functional property of population coding, and can be used for testing robustness under limited data • Mouse cortical areas are relatively high order representations, having a parallel organization rather than a sequential hierarchy, with the primary area VISp (V1) being lower order relative to the other areas Highlights VGG16 is not cherry-picked • Metrics are robust to choices of images Sub-sampled VGG16 layers vs. VGG16 layers • Mouse visual areas are relatively high order representations • Mouse visual cortex has a relatively flat hierarchy • The primary area VISp (V1) is lower order relative to the other areas

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Page 1: Comparison Against Task Driven Artificial Neural Networks ... · coding throughout the mouse visual cortex, bioRxiv (2018) [2.] Yamins, Daniel L. K. et al., Performance-Optimized

We wish to thank the Allen Institute for Brain Science founder, Paul G. Allen, for his vision, encouragement, and support.brainscience.alleninstitute.org

brain-map.org

• VGG16-pseudo-depth

• Yardstick for studying representations

• Absolute values of the metrics are sensitive to sub-sampling neurons

• O(103) neurons needed for robust comparison

Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual CortexJianghong Shi1, Eric Shea-Brown1,2, Michael A. Buice1,2

1University of Washington, 2Allen Institute for Brain Science.

• The Allen Brain Observatory presents the first standardized in vivo survey of physiological activity in the mouse visual cortex, featuring representations of visually evoked calcium responses from GCaMP6-expressing neurons in selected cortical layers, visual areas and Cre lines

• We use data from six areas of visual cortex (VISp, VISl, VISal, VISpm, VISam, VISrl) under natural scene stimuli

Allen Brain Observatory Enables Large Population Coding

Schematic of Approach

• Comparing representation matrices require proper metrics

Metric 1: Similarity of Similarity Matrices (SSM)

• PCC: Pearson correlation coefficient

• SRC: Spearman rank coefficient

Metric 2: Singular Value Canonical Correlation Analysis (SVCCA)

• Reduce neuron dimension by PCA

• Take mean of top canonical correlation coefficients

Metrics Reveal Functional Organization of Mouse Visual CortexMetrics Robustly Differentiate Deep Neural Network Layers

References[1.] de Vries, Saskia E J et al., A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex, bioRxiv (2018) [2.] Yamins, Daniel L. K. et al., Performance-Optimized Hierarchical Models Predict Neural Responses in Higher Visual Cortex, PNAS (2014) [3.] Kriegeskorte, Nikolaus et al., Representational Similarity Analysis - Connecting the Branches of Systems Neuroscience, Front Syst Neurosci. (2008) [4.] Raghu, Maithra et al., SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability, NeurIPS (2017)

Acknowledgement We acknowledge the NIH Graduate training grant in neural computation and engineering (2R90DA033461).

VGG16 layers vs. VGG16 layers with different image sets

Sub-sampled VGG19 layers vs. VGG16 layers

Mouse visual areas vs. VGG16 layers

Specific cortical layer and area vs. VGG16 layers

• The emerging large scale neural data sets usually have limited testing conditions and partially observed populations of neurons

• Deep nets reveal functional property of population coding, and can be used for testing robustness under limited data

• Mouse cortical areas are relatively high order representations, having a parallel organization rather than a sequential hierarchy, with the primary area VISp (V1) being lower order relative to the other areas

Highlights

VGG16 is not cherry-picked

• Metrics are robust to choices of images

Sub-sampled VGG16 layers vs. VGG16 layers

• Mouse visual areas are relatively high order representations • Mouse visual cortex has a relatively flat hierarchy • The primary area VISp (V1) is lower order relative to the other areas

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