Statistical characterization of cell ensembles in neuronal recordings

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“Neuronal ensembles” defined as transiently stable spatiotemporal activity patterns are not immediately obvious from physiological recordings.

In fact, it is a major scientific challenge to extract such patterns from measurements of a limited numbers of neurons.

How do we deal with variability in such patterns? When do we consider a pattern still to be signal, and when should it be regarded as noise? How can we identify repetitive motives within a space of possibilities which exponentially explodes with the number of recorded neurons? How can we ascertain the functional significance of such patterns?
These questions require interaction between experimental scientists and experts in data analysis, pattern recognition and statistics. Fred Hamprecht, Daniel Durstewitz and their colleagues devote their work to these questions, both at the conceptual level as well as by developing new statistical tools and algorithms for reliable extraction of stable patterns from our optical or electrophysiological data sets.
Quiroga-Lombard CS, Hass J, Durstewitz D (2013) Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation. J Neurophysiol 110: 562-572.

Diego F, Reichinnek S, Both M*, Hamprecht FA (2013) Automated Identification of Neuronal Activity from Calcium Imaging by Sparse Dictionary Learning. International Symposium on Biomedical Imaging (ISBI).

Diego F, Hamprecht FA (2013) Learning Multi-Level Sparse Representations. Neural Information Processing Systems (NIPS).

Hertäg L, Hass J, Golovko T, & Durstewitz D (2012) An approximation to the adaptive exponen- tial integrate-and-fire neuron model allows fast and predictive fitting to physiological data. Front Comput Neurosci 6: Article 62.

Hyman JM, Ma L, Balaguer-Ballester E, Durstewitz D§, &Seamans, JK§ (2012) Contextual encod- ing by ensembles of medial prefrontal cortex neurons. Proc Natl Acad Sci U S A 109:5086- 5091.

Andres B, Koethe U, Kroeger T, Helmstaedter M, Briggman KL, Denk W, Hamprecht FA (2012) 3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries. Med Image Anal 16:796-805.

Balaguer-Ballester E§, Lapish CCS, Seamans JK, & Durstewitz D (2011) Predictive Attractor Dy- namics of Cortical Populations During Memory-Guided Decision-Making. PLoS Comput Biol 7: e1002057. §shared first-authorship

Sommer C, Straehle C, Kothe U, Hamprecht FA (2011) ilastik: Interactive Learning and Segmen- tation Toolkit. International Symposium on Biomedical Imaging (ISBI).

Durstewitz DS, Vittoz NMS, Floresco SB, & Seamans JK (2010) Abrupt transitions between pre- frontal neural ensemble states accompany behavioral transitions during rule learning. Neuron 66:438-448. §shared first-authorship

Jaeger M, Kiel A, Herten DP, Hamprecht FA (2009) Analysis of Single-Molecule Fluorescence Spectroscopic Data with a Markov-Modulated Poisson Process. Chemphyschem 10:2486- 2495.

Lapish CC§, Durstewitz D§, Chandler LJ, & Seamans JK (2008) Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex. Proc Natl Acad Sci U S A 105:11963-11968. §shared first-authorship

Durstewitz D & Gabriel T (2007) Dynamical basis of irregular spiking in NMDA-driven prefrontal cortex neurons. Cereb Cortex 17:894-908.

*Principal investigators of other projects within the CRC