Inception loops discover what excites neurons most using deep predictive models

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Abstract

Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed ‘inception loops’, a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli—most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.

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Fig. 1: Experimental paradigm and model.
Fig. 2: MEIs.
Fig. 3: Comparison of MEIs and other types of stimuli.
Fig. 4: Neurons respond more to MEIs than other types of stimuli.

Data availability

All figures were generated from raw or processed data. The data generated and/or analyzed during the current study are available from the corresponding author upon request. No publicly available data was used in this study.

Code availability

Experiments and analyses were performed using custom software developed using the following tools: ScanImage 2018a (ref. 60), CaImAn v.1.0 (ref. 61), DataJoint v.0.11.1 (ref. 62), PyTorch v.0.4.1 (ref. 63), NumPy v.1.16.4 (ref. 64), SciPy v.1.3.0 (ref. 65), Docker v.18.09.7 (ref. 66), Matplotlib v.3.0.3 (ref. 67), seaborn v.0.9.0 (ref. 68), pandas v.0.24.2 (ref. 69) and Jupyter v.1.0.0 (ref. 70). The code for carrying out the data collection and preprocessing is available at https://github.com/cajal/pipeline; the code to perform MEI generation and analysis is available at https://github.com/cajal/inception_loop2019.

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Acknowledgements

We thank G. Denfield for comments on the manuscript. This research was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract no. D16PC00003. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC or the US Government. This research was also supported by grant no. R01 EY026927 to A.S.T, National Eyey Institute/National Institutes of Health Core Grant for Vision Research (no. T32-EY-002520-37), National Science Foundation NeuroNex grant no. 1707400 to X.P. and A.S.T., and grant no. F30EY025510 to E.Y.W. F.H.S. is supported by the Institutional Strategy of the University of Tübingen (ZUK 63) and the Carl-Zeiss-Stiftung. F.H.S. acknowledges the support from the German Federal Ministry of Education and Research (BMBF) through the Tübingen AI Center (FKZ: 01IS18039A), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-Number 2064/1 – Project number 390727645, and Amazon AWS through a Machine Learning Research Award. P.G.F. received support from the BCM Medical Scientist Training Program, no. F30-MH112312. The name of the authors’ approach, inception loops, was inspired by the movie Inception directed by Christopher Nolan.

Author information

All authors designed the experiments and developed the theoretical framework. E.Y.W. designed and implemented the inception loop framework with contributions from F.H.S. and E.C. T.M. performed the surgeries and conducted the recordings with contributions from E.F., P.G.F. and J.R. E.Y.W. performed data analyses on mice 1 and 2. E.Y.W. and E.C. performed the data analyses on mice 3–5. E.Y.W., F.H.S., A.S.E., X.P. and A.S.T. wrote the manuscript, with contributions from all authors. A.S.T. supervised all stages of the project.

Correspondence to Edgar Y. Walker or Fabian H. Sinz or Andreas S. Tolias.

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Competing interests

E.Y.W., J.R. and A.S.T. hold equity ownership in Vathes LLC, which provides development and consulting for the framework (DataJoint) used to develop and operate the data analysis pipeline for this publication.

Additional information

Peer review information Nature Neuroscience thanks Bruno Olshausen, Joel Zylberberg, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Fig. 1 Stability of MEIs.

a: MEIs are stable across initializations. MEIs for six neurons from Mouse 4 generated from four different images as the initial guess. b: Cells were reliably matched between days (left versus right) by aligning the recording planes into each stack (shown for Mouse 1). The two panels show example recording planes on separate days with a subset of the cells used to generate MEIs (colored masks). Cells with identical numbers were matched. c: MEIs are stable across days. Each block shows the MEIs of matched cells computed from models trained to predict natural image responses from scans from three separate days for Mouse 1.

Supplementary Fig. 2 Matching of cells across days.

Pearson cross-correlation of our target cells’ responses (day 1, rows in each matrix) to those of their matched cells (day N, columns in each matrix) over the test set images presented in every scan. From the five mice, a total of 2, 3, 3, 2, and 4 scans were obtained and reliably matched to the cells recorded from day 1 in that mouse. High correlations on the diagonal of the matrices suggests we were able to match cells reliably across days.

Supplementary Fig. 3 CNN models are nonlinear in non-trivial ways.

The two plots show the first ten eigenvalues of the covariance matrix of the gradients of the CNN model (blue) and the linear-nonlinear model (red) on the entire image set. Different spectra correspond to different neurons (thin lines), each was normalized to its largest eigenvalue. The average normalized spectra across neurons are indicated by the thick colored lines. As expected the LN model has a one-dimensional gradient spectrum; however, the CNN model has several eigenvalues greater than zero, demonstrating it is nonlinear in a non-trivial way.

Supplementary Fig. 4 All MEIs.

Most Exciting Inputs (MEI) for all 150 target cells in each of the five mice as they were presented back to the mouse on day 2 and beyond. Each image represents an MEI image of a distinct neuron computed from the CNN models fitted on all neurons from the same scan.

Supplementary Fig. 5 Stability of MEIs across initializations.

Stability of MEI optimization across random starting initializations for 150 target cells in Mouse 5. Left: Average pairwise Pearson correlation (μ = 0.99) across five MEIs started from different random images; correlation was restricted to pixels inside the MEI mask. Right: Highest/lowest MEI activation across five MEIs created from different random starting images (ρ = 0.99).

Supplementary Fig. 6 MEIs activate neurons with high specificity across all mice.

The confusion matrix shows responses of each neuron to the MEIs of all 150 target neurons. Responses of each neuron were normalized and pooled across days, and each row was scaled so the maximum response across all images equals 1.

Supplementary Fig. 7 MEIs have higher spatial frequency content than RFs.

The average difference in the amplitude of spatial frequency spectrum of MEIs and RFs for each of the five mice. Positive value (red) indicates spatial frequency content that is, on average, stronger in the MEIs.

Supplementary Fig. 8 All RFs.

Linear receptive fields (RF) for all 150 target cells in each of the five mice as they were presented back to the mouse on day 2 and beyond. Each image represents a RF image of a distinct neuron computed from the LN models fitted on all neurons from the same scan.

Supplementary Fig. 9 MEIs as linear filters.

Scatter plot of predictive performance of the RF used as a linear filter against the MEI used as a linear filter for the 150 target cells of Mouse 5. Performance is computed as Spearman’s rank correlation over the responses to the 100 test set images. RF consistently outperforms MEI when used as a linear filter (two-sided Wilcoxon Signed-Rank test, W = 92, \(P < 10^{ - 9}\)).

Supplementary Fig. 10 Linearized CNN model approximates LN model.

Each pair of images represents the RF from the trained LN model (left) versus the RFs from a linearized CNN model (right) for all 150 target cells in Mouse 5. The high degree of similarity between the two versions of RFs suggests that the linear component of the CNN closely approximates the linear component of neuronal population responses extracted by fitting the LN model to the responses.

Supplementary Fig. 11 MEIs and control stimuli.

The remaining MEIs and other control stimuli for Mouse 5 that were not reported in Fig. 3b. MEIs, RFs, best Gabor filters (Gabor), best masked natural images (mNI), and full natural images (fNI, ‘unmasked’ version of the best masked natural image) are shown.

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Walker, E.Y., Sinz, F.H., Cobos, E. et al. Inception loops discover what excites neurons most using deep predictive models. Nat Neurosci (2019) doi:10.1038/s41593-019-0517-x

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