Ecker, Alexander S.

[["dc.bibliographiccitation.firstpage","2060"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Nature Neuroscience"],["dc.bibliographiccitation.lastpage","2065"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Walker, Edgar Y."],["dc.contributor.author","Sinz, Fabian H."],["dc.contributor.author","Cobos, Erick"],["dc.contributor.author","Muhammad, Taliah"],["dc.contributor.author","Froudarakis, Emmanouil"],["dc.contributor.author","Fahey, Paul G."],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Reimer, Jacob"],["dc.contributor.author","Pitkow, Xaq"],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2020-03-18T10:48:38Z"],["dc.date.available","2020-03-18T10:48:38Z"],["dc.date.issued","2019"],["dc.description.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."],["dc.identifier.doi","10.1038/s41593-019-0517-x"],["dc.identifier.pmid","31686023"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63343"],["dc.language.iso","en"],["dc.relation.eissn","1546-1726"],["dc.relation.issn","1097-6256"],["dc.relation.issn","1546-1726"],["dc.title","Inception loops discover what excites neurons most using deep predictive models"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Michaelis, Claudio"],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Ecker, Alexander S."],["dc.date.accessioned","2020-03-20T09:11:18Z"],["dc.date.available","2020-03-20T09:11:18Z"],["dc.date.issued","2018"],["dc.description.abstract","We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example. We propose a novel dataset, which we call $\\textit{cluttered Omniglot}$. Using a baseline architecture combining a Siamese embedding for detection with a U-net for segmentation we show that increasing levels of clutter make the task progressively harder. Using oracle models with access to various amounts of ground-truth information, we evaluate different aspects of the problem and show that in this kind of visual search task, detection and segmentation are two intertwined problems, the solution to each of which helps solving the other. We therefore introduce $\\textit{MaskNet}$, an improved model that attends to multiple candidate locations, generates segmentation proposals to mask out background clutter and selects among the segmented objects. Our findings suggest that such image recognition models based on an iterative refinement of object detection and foreground segmentation may provide a way to deal with highly cluttered scenes."],["dc.identifier.arxiv","1803.09597v2"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63370"],["dc.language.iso","en"],["dc.title","One-Shot Segmentation in Clutter"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3730"],["dc.bibliographiccitation.lastpage","3738"],["dc.contributor.author","Gatys, Leon A."],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Hertzmann, Aaron"],["dc.contributor.author","Shechtman, Eli"],["dc.date.accessioned","2020-03-31T14:43:41Z"],["dc.date.available","2020-03-31T14:43:41Z"],["dc.date.issued","2017"],["dc.description.abstract","Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer."],["dc.identifier.doi","10.1109/CVPR.2017.397"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63421"],["dc.language.iso","en"],["dc.relation.conference","2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"],["dc.relation.eventend","2017-07-26"],["dc.relation.eventlocation","Honolulu, HI, USA"],["dc.relation.eventstart","2017-07-21"],["dc.relation.isbn","978-1-5386-0457-1"],["dc.relation.ispartof","2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"],["dc.relation.issn","1063-6919"],["dc.title","Controlling Perceptual Factors in Neural Style Transfer"],["dc.type","conference_paper"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","5"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Journal of Vision"],["dc.bibliographiccitation.lastpage","29"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Wallis, Thomas S. A."],["dc.contributor.author","Funke, Christina M."],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Gatys, Leon A."],["dc.contributor.author","Wichmann, Felix A."],["dc.contributor.author","Bethge, Matthias"],["dc.date.accessioned","2020-03-31T14:26:39Z"],["dc.date.available","2020-03-31T14:26:39Z"],["dc.date.issued","2017"],["dc.description.abstract","Our visual environment is full of texture-\"stuff\" like cloth, bark, or gravel as distinct from \"things\" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery (\"parafoveal\") and when observers were able to make eye movements to all three patches (\"inspection\"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler Portilla and Simoncelli model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the Portilla and Simoncelli model (nine compared to four textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesize textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions."],["dc.identifier.doi","10.1167/17.12.5"],["dc.identifier.pmid","28983571"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63417"],["dc.language.iso","en"],["dc.relation.eissn","1534-7362"],["dc.relation.issn","1534-7362"],["dc.title","A parametric texture model based on deep convolutional features closely matches texture appearance for humans"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1004083"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Yatsenko, Dimitri"],["dc.contributor.author","Josić, Krešimir"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Froudarakis, Emmanouil"],["dc.contributor.author","Cotton, R. James"],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2020-04-01T11:39:06Z"],["dc.date.available","2020-04-01T11:39:06Z"],["dc.date.issued","2015"],["dc.description.abstract","Ambitious projects aim to record the activity of ever larger and denser neuronal populations in vivo. Correlations in neural activity measured in such recordings can reveal important aspects of neural circuit organization. However, estimating and interpreting large correlation matrices is statistically challenging. Estimation can be improved by regularization, i.e. by imposing a structure on the estimate. The amount of improvement depends on how closely the assumed structure represents dependencies in the data. Therefore, the selection of the most efficient correlation matrix estimator for a given neural circuit must be determined empirically. Importantly, the identity and structure of the most efficient estimator informs about the types of dominant dependencies governing the system. We sought statistically efficient estimators of neural correlation matrices in recordings from large, dense groups of cortical neurons. Using fast 3D random-access laser scanning microscopy of calcium signals, we recorded the activity of nearly every neuron in volumes 200 μm wide and 100 μm deep (150-350 cells) in mouse visual cortex. We hypothesized that in these densely sampled recordings, the correlation matrix should be best modeled as the combination of a sparse graph of pairwise partial correlations representing local interactions and a low-rank component representing common fluctuations and external inputs. Indeed, in cross-validation tests, the covariance matrix estimator with this structure consistently outperformed other regularized estimators. The sparse component of the estimate defined a graph of interactions. These interactions reflected the physical distances and orientation tuning properties of cells: The density of positive 'excitatory' interactions decreased rapidly with geometric distances and with differences in orientation preference whereas negative 'inhibitory' interactions were less selective. Because of its superior performance, this 'sparse+latent' estimator likely provides a more physiologically relevant representation of the functional connectivity in densely sampled recordings than the sample correlation matrix."],["dc.identifier.doi","10.1371/journal.pcbi.1004083"],["dc.identifier.pmid","25826696"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63431"],["dc.language.iso","en"],["dc.relation.eissn","1553-7358"],["dc.relation.issn","1553-7358"],["dc.title","Improved Estimation and Interpretation of Correlations in Neural Circuits"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","062707"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Physical Review. E"],["dc.bibliographiccitation.volume","91"],["dc.contributor.author","Gatys, Leon A."],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Tchumatchenko, Tatjana"],["dc.contributor.author","Bethge, Matthias"],["dc.date.accessioned","2020-04-01T11:34:52Z"],["dc.date.available","2020-04-01T11:34:52Z"],["dc.date.issued","2015"],["dc.description.abstract","Synaptic unreliability is one of the major sources of biophysical noise in the brain. In the context of neural information processing, it is a central question how neural systems can afford this unreliability. Here we examine how synaptic noise affects signal transmission in cortical circuits, where excitation and inhibition are thought to be tightly balanced. Surprisingly, we find that in this balanced state synaptic response variability actually facilitates information transmission, rather than impairing it. In particular, the transmission of fast-varying signals benefits from synaptic noise, as it instantaneously increases the amount of information shared between presynaptic signal and postsynaptic current. Furthermore we show that the beneficial effect of noise is based on a very general mechanism which contrary to stochastic resonance does not reach an optimum at a finite noise level."],["dc.identifier.doi","10.1103/PhysRevE.91.062707"],["dc.identifier.pmid","26172736"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63430"],["dc.language.iso","en"],["dc.relation.eissn","1550-2376"],["dc.relation.issn","1539-3755"],["dc.title","Synaptic unreliability facilitates information transmission in balanced cortical populations"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","aac9462"],["dc.bibliographiccitation.issue","6264"],["dc.bibliographiccitation.journal","Science"],["dc.bibliographiccitation.volume","350"],["dc.contributor.author","Jiang, Xiaolong"],["dc.contributor.author","Shen, Shan"],["dc.contributor.author","Cadwell, Cathryn R."],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Sinz, Fabian"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Patel, Saumil"],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2020-04-01T11:50:43Z"],["dc.date.available","2020-04-01T11:50:43Z"],["dc.date.issued","2015"],["dc.description.abstract","Since the work of Ramón y Cajal in the late 19th and early 20th centuries, neuroscientists have speculated that a complete understanding of neuronal cell types and their connections is key to explaining complex brain functions. However, a complete census of the constituent cell types and their wiring diagram in mature neocortex remains elusive. By combining octuple whole-cell recordings with an optimized avidin-biotin-peroxidase staining technique, we carried out a morphological and electrophysiological census of neuronal types in layers 1, 2/3, and 5 of mature neocortex and mapped the connectivity between more than 11,000 pairs of identified neurons. We categorized 15 types of interneurons, and each exhibited a characteristic pattern of connectivity with other interneuron types and pyramidal cells. The essential connectivity structure of the neocortical microcircuit could be captured by only a few connectivity motifs."],["dc.identifier.doi","10.1126/science.aac9462"],["dc.identifier.pmid","26612957"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63434"],["dc.language.iso","en"],["dc.relation.eissn","1095-9203"],["dc.relation.issn","0036-8075"],["dc.title","Principles of connectivity among morphologically defined cell types in adult neocortex"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","242"],["dc.bibliographiccitation.lastpage","243"],["dc.contributor.author","Vinogradov, O."],["dc.contributor.author","Ecker, A. S."],["dc.date.accessioned","2020-04-02T13:27:15Z"],["dc.date.available","2020-04-02T13:27:15Z"],["dc.date.issued","2017"],["dc.description.abstract","Neurons show a high degree of variability of spike trains, even in responses to identical stimuli. This variability is often correlated between neurons of one population, however, the sources of the correlation remain unknown. According to one hypothesis, inter-trial fluctuation of an attentional signal can induce noise correlation [Cohen Newsome 2008, Ecker et al. 2016]. To test this hypothesis in the primary visual cortex, we designed a novel cued change detection task in which attentional fluctuations are modulated across trials. We trained two monkeys to maintain fixation and to make a saccade toward coherent gratings among a series of two Gabor patches with randomly changing orientations presented simultaneously in the left and right visual field. The monkeys learned to attend either to the stimulus on one side or to both stimuli (Fig. 1 A, B). To track the attentional state on a single-trial basis, we developed a model that multiplicatively accounts for the stimulus-driven variability of spikes and shared latent fluctuations of an attentional signal. The model describes the neuronal responses as a product of a stimulus response, attentional cue, slow drift, and shared latent variables (Fig. 1 C). The first two components are assumed to capture attentional modulation of the mean neuronal gain («classical» model of attention [Maunsell Treue, 2006]). The slow modulator accounts for potential drift of individual neurons’ firing rates throughout the recording session and is modeled by a Gaussian process across trials [Rabinowitz et al., 2015]. The shared attentional modulators are also assumed to be smooth, but with a faster timescale, and their within-trial …"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63536"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.relation.eventend","2017-09-15"],["dc.relation.eventlocation","Göttingen"],["dc.relation.eventstart","2017-09-13"],["dc.relation.iserratumof","yes"],["dc.title","Mixed latent variable model of attention in V1"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Berens, P."],["dc.contributor.author","Ecker, A. S."],["dc.contributor.author","Keliris, G. A."],["dc.contributor.author","Logothetis, N. K."],["dc.contributor.author","Tolias, A. S."],["dc.date.accessioned","2020-04-03T11:50:31Z"],["dc.date.available","2020-04-03T11:50:31Z"],["dc.date.issued","2007"],["dc.description.abstract","The local field potential (LFP) and, in particular, the gamma-band frequency range (30-90 Hz) have recently received much attention, as numerous studies have shown correlations between LFP and sensory, motor and cognitive variables in various cortical regions. However, the extent to which it reflects the activity of local populations of neurons remains elusive. The issue of spatial scale is central for understanding the origins of the LFP and how this signal can be used to study the functional organization of the brain. We addressed this question by simultaneously recording multi-unit spiking activity (MUA) and LFP from the primary visual cortex (V1) of awake, behaving macaques using arrays of tetrodes. Oriented gratings were used for visual stimulation, applied either binocular or monocular. The columnar organization of stimulus orientation and ocularity in V1 provides an excellent opportunity to study the spatial precision of the LFP signal, because neurons with similar orientation preference are organized at the fine spatial scale of cortical microcolumns (50-100 μm), whereas ocular dominance columns span around 450 μm. As shown before, we find that the increase of LFP gamma-band power is a function of orientation and ocularity of the stimulus. However, the power of the gamma-band contains much less information about the orientation of the stimulus than the MUA recorded at the same site. The average discriminability d'between preferred and orthogonal orientation was 2.46±0.15 for MUA and 1.01±0.05 for LFP (mean±std). Moreover, we find only a weak correlation between the preferred orientation of the MUA tuning function and that of …"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63598"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.relation.conference","37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007)"],["dc.relation.eventend","2007"],["dc.relation.eventlocation","San Diego"],["dc.relation.eventstart","2007-11-03"],["dc.relation.iserratumof","yes"],["dc.title","On the spatial scale of the local field potential-orientation and ocularity tuning of the local field potential in the primary visual cortex of the macaque"],["dc.type","conference_paper"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Cadwell, C. R."],["dc.contributor.author","Jiang, X."],["dc.contributor.author","Sinz, F. H."],["dc.contributor.author","Berens, P."],["dc.contributor.author","Fahey, P. G."],["dc.contributor.author","Yatsenko, D."],["dc.contributor.author","Froudarakis, E."],["dc.contributor.author","Ecker, A. S."],["dc.contributor.author","Cotton, R. J."],["dc.contributor.author","Tolias, A. S."],["dc.date.accessioned","2020-04-02T13:43:07Z"],["dc.date.available","2020-04-02T13:43:07Z"],["dc.date.issued","2016"],["dc.description.abstract","The neocortex carries out complex mental processes such as perception and cognition through the interactions of billions of neurons connected by trillions of synapses. Recent studies suggest that excitatory cortical neurons with a shared developmental lineage are more likely to be synaptically connected to each other than to nearby, unrelated neurons [1, 2]. However, the precise wiring diagram between clonally related neurons is unknown, and the impact of cell lineage on neural computation remains controversial. Here we show that vertical connections linking neurons across cortical layers are specifically enhanced between clonally related neurons (Fig. 1). In contrast, lateral connections within a cortical layer preferentially occur between unrelated neurons (Fig. 1). Importantly, we observed these connection biases for distantly related cousin cells, suggesting that cell lineage influences a larger fraction of connections than previously thought. A simple quantitative model of cortical connectivity based on our empirically measured connection probabilities reveals that both increased vertical connectivity and decreased lateral connectivity between cousins promote the convergence of shared input onto clonally related neurons, providing a novel circuit-level mechanism by which clonal units form functional cell assemblies with similar tuning properties [3, 4]. Taken together, our data suggest that the integration of feedforward, intra-columnar input with lateral, inter-columnar information may represent a fundamental principle of cortical computation that is established, at least initially, by developmental programs."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63538"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.relation.conference","AREADNE 2016: Research in Encoding And Decoding of Neural Ensembles"],["dc.relation.eventend","2016-06-26"],["dc.relation.eventlocation","Santorini, Greece"],["dc.relation.eventstart","2016-06-22"],["dc.relation.iserratumof","yes"],["dc.title","Cell Lineage Directs teh Precise Assembly of Excitatory Neocortical Circuits"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]