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.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","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.artnumber","e1006897"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Cadena, Santiago A."],["dc.contributor.author","Denfield, George H."],["dc.contributor.author","Walker, Edgar Y."],["dc.contributor.author","Gatys, Leon A."],["dc.contributor.author","Tolias, Andreas S."],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Ecker, Alexander S."],["dc.date.accessioned","2020-03-18T10:51:24Z"],["dc.date.available","2020-03-18T10:51:24Z"],["dc.date.issued","2019"],["dc.description.abstract","Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. We found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals."],["dc.identifier.doi","10.1371/journal.pcbi.1006897"],["dc.identifier.pmid","31013278"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63344"],["dc.language.iso","en"],["dc.relation.eissn","1553-7358"],["dc.relation.issn","1553-7358"],["dc.title","Deep convolutional models improve predictions of macaque V1 responses to natural images"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","634"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Nature Neuroscience"],["dc.bibliographiccitation.lastpage","641"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Rossant, Cyrille"],["dc.contributor.author","Kadir, Shabnam N."],["dc.contributor.author","Goodman, Dan F. M."],["dc.contributor.author","Schulman, John"],["dc.contributor.author","Hunter, Maximilian L. D."],["dc.contributor.author","Saleem, Aman B."],["dc.contributor.author","Grosmark, Andres"],["dc.contributor.author","Belluscio, Mariano"],["dc.contributor.author","Denfield, George H."],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Tolias, Andreas S."],["dc.contributor.author","Solomon, Samuel"],["dc.contributor.author","Buzsaki, Gyorgy"],["dc.contributor.author","Carandini, Matteo"],["dc.contributor.author","Harris, Kenneth D."],["dc.date.accessioned","2020-04-01T11:58:12Z"],["dc.date.available","2020-04-01T11:58:12Z"],["dc.date.issued","2016"],["dc.description.abstract","Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%."],["dc.identifier.doi","10.1038/nn.4268"],["dc.identifier.pmid","26974951"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63435"],["dc.language.iso","en"],["dc.relation.eissn","1546-1726"],["dc.relation.issn","1097-6256"],["dc.title","Spike sorting for large, dense electrode arrays"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","2654"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nature Communications"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Denfield, George H."],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Shinn, Tori J."],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Tolias, Andreas S."],["dc.date.accessioned","2022-03-01T11:45:57Z"],["dc.date.available","2022-03-01T11:45:57Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1038/s41467-018-05123-6"],["dc.identifier.pii","5123"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/103508"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-531"],["dc.relation.eissn","2041-1723"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Attentional fluctuations induce shared variability in macaque primary visual cortex"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Günthner, Max F."],["dc.contributor.author","Cadena, Santiago A."],["dc.contributor.author","Denfield, George H."],["dc.contributor.author","Walker, Edgar Y."],["dc.contributor.author","Gatys, Leon A."],["dc.contributor.author","Tolias, Andreas S."],["dc.contributor.author","Bethge, Matthias"],["dc.contributor.author","Ecker, Alexander S."],["dc.date.accessioned","2020-03-18T14:29:36Z"],["dc.date.available","2020-03-18T14:29:36Z"],["dc.date.issued","2019"],["dc.description.abstract","Divisive normalization (DN) has been suggested as a canonical computation implemented throughout the neocortex. In primary visual cortex (V1), DN was found to be crucial to explain nonlinear response properties of neurons when presented with superpositions of simple stimuli such as gratings. Based on such studies, it is currently assumed that neuronal responses to stimuli restricted to the neuron's classical receptive field (RF) are normalized by a non-specific pool of nearby neurons with similar RF locations. However, it is currently unknown how DN operates in V1 when processing natural inputs. Here, we investigated DN in monkey V1 under stimulation with natural images with an end-to-end trainable model that learns the pool of normalizing neurons and the magnitude of their contribution directly from the data. Taking advantage of our model's direct interpretable view of V1 computation, we found that oriented features were normalized preferentially by features with similar orientation preference rather than non-specifically. Our model's accuracy was competitive with state-of-the-art black-box models, suggesting that rectification, DN, and a combination of subunits resulting from DN are sufficient to account for V1 responses to localized stimuli. Thus, our work significantly advances our understanding of V1 function."],["dc.identifier.doi","10.32470/CCN.2019.1211-0"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63367"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.relation.conference","Conference on Cognitive Computational Neuroscience 2019"],["dc.relation.eventend","2019-09-16"],["dc.relation.eventlocation","Berlin, Germany"],["dc.relation.eventstart","2019-09-13"],["dc.relation.iserratumof","yes"],["dc.title","Learning Divisive Normalization in Primary Visual Cortex"],["dc.type","conference_paper"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3780"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Journal of Neurophysiology"],["dc.bibliographiccitation.lastpage","3790"],["dc.bibliographiccitation.volume","98"],["dc.contributor.author","Tolias, Andreas S."],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Siapas, Athanassios G."],["dc.contributor.author","Hoenselaar, Andreas"],["dc.contributor.author","Keliris, Georgios A."],["dc.contributor.author","Logothetis, Nikos K."],["dc.date.accessioned","2020-03-18T13:39:15Z"],["dc.date.available","2020-03-18T13:39:15Z"],["dc.date.issued","2007"],["dc.description.abstract","Understanding the mechanisms of learning requires characterizing how the response properties of individual neurons and interactions across populations of neurons change over time. To study learning in vivo, we need the ability to track an electrophysiological signature that uniquely identifies each recorded neuron for extended periods of time. We have identified such an extracellular signature using a statistical framework that allows quantification of the accuracy by which stable neurons can be identified across successive recording sessions. Our statistical framework uses spike waveform information recorded on a tetrode's four channels to define a measure of similarity between neurons recorded across time. We use this framework to quantitatively demonstrate for the first time the ability to record from the same neurons across multiple consecutive days and weeks. The chronic recording techniques and methods of analyses we report can be used to characterize the changes in brain circuits due to learning."],["dc.identifier.doi","10.1152/jn.00260.2007"],["dc.identifier.pmid","17942615"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63359"],["dc.language.iso","en"],["dc.relation.issn","0022-3077"],["dc.title","Recording Chronically From the Same Neurons in Awake, Behaving Primates"],["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"]]