# Neural Data Analysis

We are interested in how neural circuits in sensory systems perform the computations necessary to mediate behavior. To make progress on this question , we develop machine learning tools, statistical modeling approaches and data analytics solutions for making sense of the large scale high-dimensional data sets acquired by our experimental collaborators (visual cortex: Tolias lab; retina: Euler lab, barrel system: Schwarz lab). The following examples are representative of the type of work we do in this area:

- We used
**state space models**for analysing the effect of anesthesia on the structure of population activity (Figure 1, Ecker et al. 2014). We showed that anesthesia-induced network state fluctuations lead to correlated variability and accounting for the network state recovers the awake noise correlation structure. This analysis allowed us thus to identify a major source of the controversy surrounding noise correlations in V1. In addition, we demonstrated that it is possible to infer internal brain signals l in real time from neural population activity, which has broad applications for example in studying the effects of attention or motivation on neural population activity. - We developed a new algorithm for
**inferring firing rates from calcium imaging data**as acquired in two-photon population imaging experiments based on flexible probabilistic models (Theis et al. 2015). Importantly, we show in the first extensive benchmark comparison to date on >50 cells for which ground truth is known that our algorithm outperforms all previoulsy published algorithms, even on datasets not seen during training.

## Selected References

L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. Roman-Roson, T. Baden, T. Euler, A. S. Tolias,*et al.*

**Benchmarking spike rate inference in population calcium imaging**

*Neuron,*

**90**(3), 471-482, 2016#two-photon imaging, #spiking neurons

Code, URL, DOI, BibTex

E. Froudarakis, P. Berens, A. S. Ecker, R. J. Cotton, F. H. Sinz, D. Yatsenko, P. Saggau, M. Bethge,

*et al.*

**Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness**

*Nature Neuroscience,*

**17**, 851-857, 2014#sparsity, #natural image statistics, #population coding, #v1, #two-photon imaging

URL, DOI, PDF, BibTex

A. S. Ecker, P. Berens, R. J. Cotton, M. Subramaniyan, G. H. Denfield, C. R. Cadwell, S. M. Smirnakis, M. Bethge,

*et al.*

**State dependence of noise correlations in macaque primary visual cortex**

*Neuron,*

**82**(1), 235-248, 2014#noise correlations, #gpfa, #population, #anesthesia, #macaque

Code, URL, DOI, PDF, BibTex

L. Theis, A. M. Chagas, D. Arnstein, C. Schwarz, and M. Bethge

**Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification**

*PLoS Computational Biology,*

**9**(11), 2013#generalized linear model, #spiking neurons, #mixture models

Code, URL, DOI, PDF, BibTex

T. Baden, P. Berens, M. Bethge, and T. Euler

**Spikes in Mammalian Bipolar Cells Support Temporal Layering of the Inner Retina**

*Current Biology,*

**23**(1), 48-52, 2013#retina, #bipolar cells, #population coding

URL, DOI, PDF, BibTex

S. Gerwinn, J. Macke, and M. Bethge

**Bayesian inference for generalized linear models for spiking neurons**

*Frontiers in Computational Neuroscience,*

**4**, 2010#bayesian inference, #generalized linear model, #spiking neurons

Code, URL, DOI, PDF, BibTex

J. H. Macke, S. Gerwinn, L. White, M. Kaschube, and M. Bethge

**Gaussian process methods for estimating cortical maps**

*NeuroImage,*

**56**(2), 570-581, 2010#gaussian process

Code, URL, DOI, PDF, BibTex