Ecker, Alexander S.

[["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","10618"],["dc.bibliographiccitation.issue","31"],["dc.bibliographiccitation.journal","The Journal of Neuroscience"],["dc.bibliographiccitation.lastpage","10626"],["dc.bibliographiccitation.volume","32"],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Cotton, R. James"],["dc.contributor.author","Ma, Wei Ji"],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2020-03-18T13:25:24Z"],["dc.date.available","2020-03-18T13:25:24Z"],["dc.date.issued","2012"],["dc.description.abstract","Orientation tuning has been a classic model for understanding single-neuron computation in the neocortex. However, little is known about how orientation can be read out from the activity of neural populations, in particular in alert animals. Our study is a first step toward that goal. We recorded from up to 20 well isolated single neurons in the primary visual cortex of alert macaques simultaneously and applied a simple, neurally plausible decoder to read out the population code. We focus on two questions: First, what are the time course and the timescale at which orientation can be read out from the population response? Second, how complex does the decoding mechanism in a downstream neuron have to be to reliably discriminate between visual stimuli with different orientations? We show that the neural ensembles in primary visual cortex of awake macaques represent orientation in a way that facilitates a fast and simple readout mechanism: With an average latency of 30-80 ms, the population code can be read out instantaneously with a short integration time of only tens of milliseconds, and neither stimulus contrast nor correlations need to be taken into account to compute the optimal synaptic weight pattern. Our study shows that-similar to the case of single-neuron computation-the representation of orientation in the spike patterns of neural populations can serve as an exemplary case for understanding the computations performed by neural ensembles underlying visual processing during behavior."],["dc.identifier.doi","10.1523/JNEUROSCI.1335-12.2012"],["dc.identifier.pmid","22855811"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63353"],["dc.language.iso","en"],["dc.relation.eissn","1529-2401"],["dc.relation.issn","0270-6474"],["dc.relation.issn","1529-2401"],["dc.title","A Fast and Simple Population Code for Orientation in Primate V1"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","851"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Nature Neuroscience"],["dc.bibliographiccitation.lastpage","857"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Froudarakis, Emmanouil"],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Cotton, R. James"],["dc.contributor.author","Sinz, Fabian H."],["dc.contributor.author","Yatsenko, Dimitri"],["dc.contributor.author","Saggau, Peter"],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2020-03-18T13:23:31Z"],["dc.date.available","2020-03-18T13:23:31Z"],["dc.date.issued","2014"],["dc.description.abstract","Neural codes are believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher order correlations in natural scenes induced a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits."],["dc.identifier.doi","10.1038/nn.3707"],["dc.identifier.pmid","24747577"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63352"],["dc.language.iso","en"],["dc.relation.eissn","1546-1726"],["dc.relation.issn","1097-6256"],["dc.relation.issn","1546-1726"],["dc.title","Population code in mouse V1 facilitates readout of natural scenes through increased sparseness"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","397"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Neural Computation"],["dc.bibliographiccitation.lastpage","423"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Macke, Jakob H."],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Tolias, Andreas S."],["dc.contributor.author","Bethge, Matthias"],["dc.date.accessioned","2020-03-18T13:32:26Z"],["dc.date.available","2020-03-18T13:32:26Z"],["dc.date.issued","2009"],["dc.description.abstract","Spike trains recorded from populations of neurons can exhibit substantial pairwise correlations between neurons and rich temporal structure. Thus, for the realistic simulation and analysis of neural systems, it is essential to have efficient methods for generating artificial spike trains with specified correlation structure. Here we show how correlated binary spike trains can be simulated by means of a latent multivariate gaussian model. Sampling from the model is computationally very efficient and, in particular, feasible even for large populations of neurons. The entropy of the model is close to the theoretical maximum for a wide range of parameters. In addition, this framework naturally extends to correlations over time and offers an elegant way to model correlated neural spike counts with arbitrary marginal distributions."],["dc.identifier.doi","10.1162/neco.2008.02-08-713"],["dc.identifier.pmid","19196233"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63357"],["dc.language.iso","en"],["dc.relation.issn","0899-7667"],["dc.title","Generating Spike Trains with Specified Correlation Coefficients"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Yatsenko, Dimitri"],["dc.contributor.author","Reimer, Jacob"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Walker, Edgar Y."],["dc.contributor.author","Sinz, Fabian"],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Hoenselaar, Andreas"],["dc.contributor.author","Cotton, Ronald James"],["dc.contributor.author","Siapas, Athanassios S."],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2020-04-01T11:48:43Z"],["dc.date.available","2020-04-01T11:48:43Z"],["dc.date.issued","2015"],["dc.description.abstract","The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. Here we describe DataJoint, an open-source toolbox designed for manipulating and processing scientific data under the relational data model. Designed for scientists who need a flexible and expressive database language with few basic concepts and operations, DataJoint facilitates multiuser access, efficient queries, and distributed computing. With implementations in both MATLAB and Python, DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com."],["dc.identifier.doi","10.1101/031658"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63433"],["dc.language.iso","en"],["dc.title","DataJoint: managing big scientific data using MATLAB or Python"],["dc.type","preprint"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Gerwinn, Sebastian"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Bethge, Matthias"],["dc.date.accessioned","2020-04-01T11:10:54Z"],["dc.date.available","2020-04-01T11:10:54Z"],["dc.date.issued","2009"],["dc.identifier.doi","10.3389/conf.neuro.10.2009.14.093"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63423"],["dc.language.iso","en"],["dc.relation.issn","1662-5188"],["dc.title","Neurometric function analysis of short-term population codes"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2430"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Neurophysiology"],["dc.bibliographiccitation.lastpage","2452"],["dc.bibliographiccitation.volume","120"],["dc.contributor.author","Subramaniyan, Manivannan"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Patel, Saumil S."],["dc.contributor.author","Cotton, R. James"],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Pitkow, Xaq"],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2020-03-18T13:09:19Z"],["dc.date.available","2020-03-18T13:09:19Z"],["dc.date.issued","2018"],["dc.description.abstract","When the brain has determined the position of a moving object, because of anatomical and processing delays the object will have already moved to a new location. Given the statistical regularities present in natural motion, the brain may have acquired compensatory mechanisms to minimize the mismatch between the perceived and real positions of moving objects. A well-known visual illusion-the flash lag effect-points toward such a possibility. Although many psychophysical models have been suggested to explain this illusion, their predictions have not been tested at the neural level, particularly in a species of animal known to perceive the illusion. To this end, we recorded neural responses to flashed and moving bars from primary visual cortex (V1) of awake, fixating macaque monkeys. We found that the response latency to moving bars of varying speed, motion direction, and luminance was shorter than that to flashes, in a manner that is consistent with psychophysical results. At the level of V1, our results support the differential latency model positing that flashed and moving bars have different latencies. As we found a neural correlate of the illusion in passively fixating monkeys, our results also suggest that judging the instantaneous position of the moving bar at the time of flash-as required by the postdiction/motion-biasing model-may not be necessary for observing a neural correlate of the illusion. Our results also suggest that the brain may have evolved mechanisms to process moving stimuli faster and closer to real time compared with briefly appearing stationary stimuli. NEW & NOTEWORTHY We report several observations in awake macaque V1 that provide support for the differential latency model of the flash lag illusion. We find that the equal latency of flash and moving stimuli as assumed by motion integration/postdiction models does not hold in V1. We show that in macaque V1, motion processing latency depends on stimulus luminance, speed and motion direction in a manner consistent with several psychophysical properties of the flash lag illusion."],["dc.identifier.doi","10.1152/jn.00792.2017"],["dc.identifier.pmid","30365390"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63346"],["dc.language.iso","en"],["dc.relation.eissn","1522-1598"],["dc.relation.issn","0022-3077"],["dc.relation.issn","1522-1598"],["dc.title","Faster processing of moving compared with flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","235"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Neuron"],["dc.bibliographiccitation.lastpage","248"],["dc.bibliographiccitation.volume","82"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Cotton, R. James"],["dc.contributor.author","Subramaniyan, Manivannan"],["dc.contributor.author","Denfield, George H."],["dc.contributor.author","Cadwell, Cathryn R."],["dc.contributor.author","Smirnakis, Stelios M."],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2020-04-01T11:30:12Z"],["dc.date.available","2020-04-01T11:30:12Z"],["dc.date.issued","2014"],["dc.description.abstract","Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.4, with little consensus on which factors account for these differences. Here we addressed one important factor that varied across studies, asking how anesthesia affects the population activity structure in macaque primary visual cortex. We found that under opioid anesthesia, activity was dominated by strong coordinated fluctuations on a timescale of 1-2 Hz, which were mostly absent in awake, fixating monkeys. Accounting for these global fluctuations markedly reduced correlations under anesthesia, matching those observed during wakefulness and reconciling earlier studies conducted under anesthesia and in awake animals. Our results show that internal signals, such as brain state transitions under anesthesia, can induce noise correlations but can also be estimated and accounted for based on neuronal population activity."],["dc.identifier.doi","10.1016/j.neuron.2014.02.006"],["dc.identifier.pmid","2469-8278"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63428"],["dc.language.iso","en"],["dc.relation.eissn","1097-4199"],["dc.relation.issn","0896-6273"],["dc.title","State Dependence of Noise Correlations in Macaque Primary Visual Cortex"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Nature Precedings"],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Ecker, Alexander"],["dc.contributor.author","Gerwinn, Sebastian"],["dc.contributor.author","Tolias, Andreas"],["dc.contributor.author","Bethge, Matthias"],["dc.date.accessioned","2020-04-01T11:21:20Z"],["dc.date.available","2020-04-01T11:21:20Z"],["dc.date.issued","2011"],["dc.description.abstract","Cortical circuits perform the computations underlying rapid perceptual decisions within a few dozen milliseconds with each neuron emitting only a few spikes. Under these conditions, the theoretical analysis of neural population codes is challenging, as the most commonly used theoretical tool – Fisher information – can lead to erroneous conclusions about the optimality of different coding schemes. Here we revisit the effect of tuning function width and correlation structure on neural population codes based on ideal observer analysis in both a discrimination and reconstruction task. We show that the optimal tuning function width and the optimal correlation structure in both paradigms strongly depend on the available decoding time in a very similar way. In contrast, population codes optimized for Fisher information do not depend on decoding time and are severely suboptimal when only few spikes are available. In addition, we use the neurometric functions of the ideal observer in the classification task to investigate the differential coding properties of these Fisher-optimal codes for fine and coarse discrimination. We find that the discrimination error for these codes does not decrease to zero with increasing population size, even in simple coarse discrimination tasks. Our results suggest that quite different population codes may be optimal for rapid decoding in cortical computations than those inferred from the optimization of Fisher information."],["dc.identifier.doi","10.1038/npre.2011.5513.1"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63426"],["dc.language.iso","en"],["dc.relation.issn","1756-0357"],["dc.title","Optimal Population Coding, Revisited"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Zhao, Zhijian"],["dc.contributor.author","Klindt, David"],["dc.contributor.author","Chagas, André Maia"],["dc.contributor.author","Szatko, Klaudia P."],["dc.contributor.author","Rogerson, Luke"],["dc.contributor.author","Protti, Dario A."],["dc.contributor.author","Behrens, Christian"],["dc.contributor.author","Dalkara, Deniz"],["dc.contributor.author","Schubert, Timm"],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Franke, Katrin"],["dc.contributor.author","Berens, Philipp"],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Euler, Thomas"],["dc.date.accessioned","2020-03-18T14:25:45Z"],["dc.date.available","2020-03-18T14:25:45Z"],["dc.date.issued","2019"],["dc.description.abstract","The retina decomposes visual stimuli into parallel channels that encode different features of the visual environment. Central to this computation is the synaptic processing in a dense and thick layer of neuropil, the so-called inner plexiform layer (IPL). Here, different types of bipolar cells stratifying at distinct depths relay the excitatory feedforward drive from photoreceptors to amacrine and ganglion cells. Current experimental techniques for studying processing in the IPL do not allow imaging the entire IPL simultaneously in the intact tissue. Here, we extend a two-photon microscope with an electrically tunable lens allowing us to obtain optical vertical slices of the IPL, which provide a complete picture of the response diversity of bipolar cells at a \"single glance\". The nature of these axial recordings additionally allowed us to isolate and investigate batch effects, i.e. inter-experimental variations resulting in systematic differences in response speed. As a proof of principle, we developed a simple model that disentangles biological from experimental causes of variability, and allowed us to recover the characteristic gradient of response speeds across the IPL with higher precision than before. Our new framework will make it possible to study the computations performed in the central synaptic layer of the retina more efficiently."],["dc.identifier.doi","10.1101/743047"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63366"],["dc.language.iso","en"],["dc.title","The temporal structure of the inner retina at a single glance"],["dc.type","preprint"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]