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Large scale optical physiology: From data-acquisition to models of neural coding
Il Memming Park · Jakob H Macke · Ferran Diego Andilla · Eftychios Pnevmatikakis · Jeremy Freeman

Fri Dec 12 05:30 AM -- 03:30 PM (PST) @ Level 5; room 514 a, b
Event URL: http://hci.iwr.uni-heidelberg.de//Staff/fdiego/LargeScaleOpticalPhysiology/ »

A detailed understanding of brain function is a still-elusive grand challenge. Major advances in recording technologies (e.g. 2-photon and light-sheet microscopic imaging of calcium signals) are now beginning to provide measurements of neural activity at an unprecedented size and quality. Computational tools will be of critical importance both for the high-throughput acquisition and analysis of large-scale datasets. Reliable and robust tools for automated high-throughput analysis of such data that works have not been available so far. As a consequence, experimental reality is still characterized by semi-manual analysis or makeshift scripts that are specialized to a single setting. Similarly, many analysis still focus on the response properties of single neurons or on pairwise correlations across neurons, thereby potentially missing information which is only available at the population level.

The goal of this workshop is to discuss challenges and opportunities for
computational neuroscience and machine learning which arise from large-scale recording techniques:

* What kind of data will be generated by large-scale functional measurements in the next decade? How will it be quantitatively or qualitatively different to the kind of data we have had previously? What are the computational bottlenecks for their analysis?

* What kind of computational tools play an important role on high-throughput data acquisition, e. g. visualization/dimensionality reduction/information quantification? How can we figure out which algorithms work best, and which are the important challenges that are not met by existing techniques?

* What have we really learned from high-dimensional recordings that is new? What theories could we test, if only we had access to recordings from more neurons at the same time? What kind of statistics will be powerful enough to verify/falsify population coding theories? What can we infer about the network structure and dynamics?

We have invited scientists whose research addresses these questions, including researchers developing recording technologies, experimental and computational neuroscientists. We foresee active discussions amongst this multidisciplinary group of scientists to create a chance to discuss priorities and perspective, debate about the currently most relevant problems in the field, and emphasize the most promising future research directions. The target audience of this workshop includes industry and academic researchers interested in machine learning, neuroscience, big data and statistical inference.

Author Information

Il Memming Park (Stony Brook University)
Jakob H Macke (research center caesar, an associate of the Max Planck Society)
Ferran Diego Andilla (Telefonica R&D)
Eftychios Pnevmatikakis (Flatiron Institute)
Jeremy Freeman (HHMI Janelia Farm)

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