The Experts below are selected from a list of 63384 Experts worldwide ranked by ideXlab platform
Surya Ganguli - One of the best experts on this subject based on the ideXlab platform.
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Inferring Hidden Structure in multilayered neural circuits.
PLOS Computational Biology, 2018Co-Authors: Niru Maheswaranathan, David B. Kastner, Stephen A Baccus, Surya GanguliAbstract:A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field Structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.
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Inferring Hidden Structure in multilayered neural circuits
bioRxiv, 2017Co-Authors: Niru Maheswaranathan, Stephen A Baccus, Surya GanguliAbstract:A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across multiple nonlinear cell layers. Here we develop a computational framework to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit. We combine non-smooth regularization techniques with proximal consensus algorithms to overcome traditional difficulties in fitting such models due to the high dimensionality of their parameter space. Our methods are both statistically and computationally efficient, enabling us to not only rapidly learn hierarchical non-linear models, but also efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. We apply our framework to retinal ganglion cell processing, learning STAs and STCs to similar accuracy using just 12% of recorded data, reducing experiment time by an order of magnitude. Furthermore, we learn three layer nonlinear models of retinal circuitry, consisting of thousands of parameters, using only 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. The internal Structure of these models reveals that Hidden nonlinear subunits match the properties of retinal bipolar cells in both receptive field Structure and number. Subunits had consistently high thresholds, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model9s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space-a central tenet of efficient coding theory-originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.
Danilo Bzdok - One of the best experts on this subject based on the ideXlab platform.
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Patterns of autism symptoms: Hidden Structure in the ADOS and ADI-R instruments
Translational psychiatry, 2020Co-Authors: Jeremy Lefort-besnard, Kai Vogeley, Leonhard Schilbach, Gaël Varoquaux, Bertrand Thirion, Guillaume Dumas, Danilo BzdokAbstract:We simultaneously revisited the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) with a comprehensive data-analytics strategy. Here, the combination of pattern-analysis algorithms and extensive data resources (n = 266 patients aged 7–49 years) allowed identifying coherent clinical constellations in and across ADI-R and ADOS assessments widespread in clinical practice. Our clustering approach revealed low- and high-severity patient groups, as well as a group scoring high only in the ADI-R domains, providing quantitative contours for the widely assumed autism subtypes. Sparse regression approaches uncovered the most clinically predictive questionnaire domains. The social and communication domains of the ADI-R showed convincing performance to predict the patients’ symptom severity. Finally, we explored the relative importance of each of the ADI-R and ADOS domains conditioning on age, sex, and fluid IQ in our sample. The collective results suggest that (i) identifying autism subtypes and severity for a given individual may be most manifested in the ADI-R social and communication domains, (ii) the ADI-R might be a more appropriate tool to accurately capture symptom severity, and (iii) the ADOS domains were more relevant than the ADI-R domains to capture sex differences.
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patterns of autism symptoms Hidden Structure in the ados and adi r instruments
medRxiv, 2019Co-Authors: Jeremy Lefortbesnard, Kai Vogeley, Leonhard Schilbach, Gaël Varoquaux, Bertrand Thirion, Guillaume Dumas, Danilo BzdokAbstract:ABSTRACT We simultaneously revisited the ADI-R and ADOS with a comprehensive data-analytics strategy. Here, the combination of pattern analysis algorithms and an extensive data resources (n=266 patients aged 7 to 49 years) allowed identifying coherent clinical constellations in and across ADI-R and ADOS assessments widespread in clinical practice. The collective results of the clustering and sparse regression approaches suggest that identifying autism subtypes and severity for a given individual may be most manifested in the social and communication domains of the ADI-R. Additionally, our quantitative investigation revealed that disease-specific patterns of ADI-R and ADOS scores can be uncovered when studying sex, age or level of FIQ in patients.
Avinatan Hassidim - One of the best experts on this subject based on the ideXlab platform.
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super polynomial quantum speed ups for boolean evaluation trees with Hidden Structure
Conference on Innovations in Theoretical Computer Science, 2012Co-Authors: Bohua Zhan, Shelby Kimmel, Avinatan HassidimAbstract:We give a quantum algorithm for evaluating a class of boolean formulas (such as NAND trees and 3-majority trees) on a restricted set of inputs. Due to the Structure of the allowed inputs, our algorithm can evaluate a depth n tree using O(n2+logω) queries, where ω is independent of n and depends only on the type of subformulas within the tree. We also prove a classical lower bound of nΩ(log log n) queries, thus showing a (small) super-polynomial speed-up.
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super polynomial quantum speed ups for boolean evaluation trees with Hidden Structure
arXiv: Quantum Physics, 2011Co-Authors: Bohua Zhan, Shelby Kimmel, Avinatan HassidimAbstract:We give a quantum algorithm for evaluating a class of boolean formulas (such as NAND trees and 3-majority trees) on a restricted set of inputs. Due to the Structure of the allowed inputs, our algorithm can evaluate a depth $n$ tree using $O(n^{2+\log\omega})$ queries, where $\omega$ is independent of $n$ and depends only on the type of subformulas within the tree. We also prove a classical lower bound of $n^{\Omega(\log\log n)}$ queries, thus showing a (small) super-polynomial speed-up.
Niru Maheswaranathan - One of the best experts on this subject based on the ideXlab platform.
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Inferring Hidden Structure in multilayered neural circuits.
PLOS Computational Biology, 2018Co-Authors: Niru Maheswaranathan, David B. Kastner, Stephen A Baccus, Surya GanguliAbstract:A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field Structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.
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Inferring Hidden Structure in multilayered neural circuits
bioRxiv, 2017Co-Authors: Niru Maheswaranathan, Stephen A Baccus, Surya GanguliAbstract:A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across multiple nonlinear cell layers. Here we develop a computational framework to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit. We combine non-smooth regularization techniques with proximal consensus algorithms to overcome traditional difficulties in fitting such models due to the high dimensionality of their parameter space. Our methods are both statistically and computationally efficient, enabling us to not only rapidly learn hierarchical non-linear models, but also efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. We apply our framework to retinal ganglion cell processing, learning STAs and STCs to similar accuracy using just 12% of recorded data, reducing experiment time by an order of magnitude. Furthermore, we learn three layer nonlinear models of retinal circuitry, consisting of thousands of parameters, using only 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. The internal Structure of these models reveals that Hidden nonlinear subunits match the properties of retinal bipolar cells in both receptive field Structure and number. Subunits had consistently high thresholds, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model9s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space-a central tenet of efficient coding theory-originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.
Tiago P Peixoto - One of the best experts on this subject based on the ideXlab platform.
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nonparametric bayesian inference of the microcanonical stochastic block model
Physical Review E, 2017Co-Authors: Tiago P PeixotoAbstract:A principled approach to characterize the Hidden Structure of networks is to formulate generative models and then infer their parameters from data. When the desired Structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular Structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the Structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal Structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
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inferring the mesoscale Structure of layered edge valued and time varying networks
Physical Review E, 2015Co-Authors: Tiago P PeixotoAbstract:Many network systems are composed of interdependent but distinct types of interactions, which cannot be fully understood in isolation. These different types of interactions are often represented as layers, attributes on the edges, or as a time dependence of the network Structure. Although they are crucial for a more comprehensive scientific understanding, these representations offer substantial challenges. Namely, it is an open problem how to precisely characterize the large or mesoscale Structure of network systems in relation to these additional aspects. Furthermore, the direct incorporation of these features invariably increases the effective dimension of the network description, and hence aggravates the problem of overfitting, i.e., the use of overly complex characterizations that mistake purely random fluctuations for actual Structure. In this work, we propose a robust and principled method to tackle these problems, by constructing generative models of modular network Structure, incorporating layered, attributed and time-varying properties, as well as a nonparametric Bayesian methodology to infer the parameters from data and select the most appropriate model according to statistical evidence. We show that the method is capable of revealing Hidden Structure in layered, edge-valued, and time-varying networks, and that the most appropriate level of granularity with respect to the additional dimensions can be reliably identified. We illustrate our approach on a variety of empirical systems, including a social network of physicians, the voting correlations of deputies in the Brazilian national congress, the global airport network, and a proximity network of high-school students.