Liu, Jian K.

[["dc.bibliographiccitation.artnumber","e1005189"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Onken, Arno"],["dc.contributor.author","Liu, Jian K."],["dc.contributor.author","Karunasekara, P. P. Chamanthi R."],["dc.contributor.author","Delis, Ioannis"],["dc.contributor.author","Gollisch, Tim"],["dc.contributor.author","Panzeri, Stefano"],["dc.date.accessioned","2018-11-07T10:06:04Z"],["dc.date.available","2018-11-07T10:06:04Z"],["dc.date.issued","2016"],["dc.description.abstract","Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding."],["dc.identifier.doi","10.1371/journal.pcbi.1005189"],["dc.identifier.isi","000391230900026"],["dc.identifier.pmid","27814363"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13941"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39022"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1553-7358"],["dc.relation.issn","1553-734X"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","149"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nature Communications"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Liu, Jian K."],["dc.contributor.author","Schreyer, Helene M."],["dc.contributor.author","Onken, Arno"],["dc.contributor.author","Rozenblit, Fernando"],["dc.contributor.author","Khani, Mohammad H."],["dc.contributor.author","Krishnamoorthy, Vidhyasankar"],["dc.contributor.author","Panzeri, Stefano"],["dc.contributor.author","Gollisch, Tim"],["dc.date.accessioned","2019-02-27T09:39:23Z"],["dc.date.available","2019-02-27T09:39:23Z"],["dc.date.issued","2017"],["dc.description.abstract","Neurons in sensory systems often pool inputs over arrays of presynaptic cells, giving rise to functional subunits inside a neuron's receptive field. The organization of these subunits provides a signature of the neuron's presynaptic functional connectivity and determines how the neuron integrates sensory stimuli. Here we introduce the method of spike-triggered non-negative matrix factorization for detecting the layout of subunits within a neuron's receptive field. The method only requires the neuron's spiking responses under finely structured sensory stimulation and is therefore applicable to large populations of simultaneously recorded neurons. Applied to recordings from ganglion cells in the salamander retina, the method retrieves the receptive fields of presynaptic bipolar cells, as verified by simultaneous bipolar and ganglion cell recordings. The identified subunit layouts allow improved predictions of ganglion cell responses to natural stimuli and reveal shared bipolar cell input into distinct types of ganglion cells.How a neuron integrates sensory information requires knowledge about its functional presynaptic connections. Here the authors report a new method using non-negative matrix factorization to identify the layout of presynaptic bipolar cell inputs onto retinal ganglion cells and predict their responses to natural stimuli."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2017"],["dc.identifier.doi","10.1038/s41467-017-00156-9"],["dc.identifier.eissn","2041-1723"],["dc.identifier.pmid","28747662"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14543"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57634"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]