scholarly journals Using computational theory to constrain statistical models of neural data

2017 ◽  
Author(s):  
Scott W. Linderman ◽  
Samuel J. Gershman

AbstractComputational neuroscience is, to first order, dominated by two approaches: the “bottom-up” approach, which searches for statistical patterns in large-scale neural recordings, and the “top-down” approach, which begins with a theory of computation and considers plausible neural implementations. While this division is not clear-cut, we argue that these approaches should be much more intimately linked. From a Bayesian perspective, computational theories provide constrained prior distributions on neural data—albeit highly sophisticated ones. By connecting theory to observation via a probabilistic model, we provide the link necessary to test, evaluate, and revise our theories in a data-driven and statistically rigorous fashion. This review highlights examples of this theory-driven pipeline for neural data analysis in recent literature and illustrates it with a worked example based on the temporal difference learning model of dopamine.


2018 ◽  
Author(s):  
Emily L. Mackevicius ◽  
Andrew H. Bahle ◽  
Alex H. Williams ◽  
Shijie Gu ◽  
Natalia I. Denissenko ◽  
...  

AbstractIdentifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.



2020 ◽  
Author(s):  
Matthew G. Perich ◽  
Charlotte Arlt ◽  
Sofia Soares ◽  
Megan E. Young ◽  
Clayton P. Mosher ◽  
...  

ABSTRACTBehavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of interregion communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.



eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Emily L Mackevicius ◽  
Andrew H Bahle ◽  
Alex H Williams ◽  
Shijie Gu ◽  
Natalia I Denisenko ◽  
...  

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.



2021 ◽  
Author(s):  
Joshua P Chu ◽  
Caleb Kemere

Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large scale modalities. Here we introduce ghostipy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. ghostipy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.



Author(s):  
Osama Abdelkarim ◽  
Julian Fritsch ◽  
Darko Jekauc ◽  
Klaus Bös

Physical fitness is an indicator for children’s public health status. Therefore, the aim of this study was to examine the construct validity and the criterion-related validity of the German motor test (GMT) in Egyptian schoolchildren. A cross-sectional study was conducted with a total of 931 children aged 6 to 11 years (age: 9.1 ± 1.7 years) with 484 (52%) males and 447 (48%) females in grades one to five in Assiut city. The children’s physical fitness data were collected using GMT. GMT is designed to measure five health-related physical fitness components including speed, strength, coordination, endurance, and flexibility of children aged 6 to 18 years. The anthropometric data were collected based on three indicators: body height, body weight, and BMI. A confirmatory factor analysis was conducted with IBM SPSS AMOS 26.0 using full-information maximum likelihood. The results indicated an adequate fit (χ2 = 112.3, df = 20; p < 0.01; CFI = 0.956; RMSEA = 0.07). The χ2-statistic showed significant results, and the values for CFI and RMSEA showed a good fit. All loadings of the manifest variables on the first-order latent factors as well as loadings of the first-order latent factors on the second-order superordinate factor were significant. The results also showed strong construct validity in the components of conditioning abilities and moderate construct validity in the components of coordinative abilities. GMT proved to be a valid method and could be widely used on large-scale studies for health-related fitness monitoring in the Egyptian population.



2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Zong-Gang Mou ◽  
Paul M. Saffin ◽  
Anders Tranberg

Abstract We perform large-scale real-time simulations of a bubble wall sweeping through an out-of-equilibrium plasma. The scenario we have in mind is the electroweak phase transition, which may be first order in extensions of the Standard Model, and produce such bubbles. The process may be responsible for baryogenesis and can generate a background of primordial cosmological gravitational waves. We study thermodynamic features of the plasma near the advancing wall, the generation of Chern-Simons number/Higgs winding number and consider the potential for CP-violation at the wall generating a baryon asymmetry. A number of technical details necessary for a proper numerical implementation are developed.



2021 ◽  
Vol 70 ◽  
pp. 64-73
Author(s):  
Cole Hurwitz ◽  
Nina Kudryashova ◽  
Arno Onken ◽  
Matthias H. Hennig


Author(s):  
Hugh P. Taylor

ABSTRACTOxygen isotope data are very useful in determining the source rocks of granitic magmas, particularly when used in combination with Sr, Pb, and Nd isotope studies. For example, unusually high δ18O values in magmas (δ18O> +8) require the involvement of some precursor parent material that at some time in the past resided on or near the Earth's surface, either as sedimentary rocks or as weathered or hydrothermally altered rocks. The isotopic systematics which are preserved in the Mesozoic and Cenozoic batholiths of western North America can be explained by grand-scale mixing of three broadly defined end-members: (1) oceanic island-arc magmas derived from a “depleted” (MORB-type?) source in the upper mantle (δ18O c. +6 and 87Sr/86Sr c. 0·703); (2) a high-18O (c. +13 to +17) source with a very uniform 87Sr/86Sr (c. 0·708 to 0·712), derived mainly from eugeosynclinal volcanogenic sediments and (or) hydrothermally altered basalts; and (3) a much more heterogeneous source (87Sr/86Sr c. 0·706 to 0·750, or higher) with a high δ18O (c. +9 to +15) where derived from supracrustal metasedimentary rocks and a much lower δ18O (c. +7 to +9) where derived from the lower continental crust of the craton. These end-members were successively dominant from W to E, respectively, within three elongate N–S geographic zones that can be mapped from Mexico all the way N to Idaho.18O/16O studies (together with D/H analyses) can, however, play a more important and certainly a unique role in determining the origins of the aqueous fluids involved in the formation of granitic and rhyolitic magmas. Fluid-rock interaction effects are most clear-cut when low-18O, low-D meteoric waters are involved in the isotopic exchange and melting processes, but the effects of other waters such as seawater (with a relatively high δD c. 0) can also be recognised. Because of these hydrothermal processes, rocks that ultimately undergo partial melting may exhibit isotopic signatures considerably different from those that they started with. We discuss three broad classes of potential source materials of such “hydrothermal-anatectic” granitic magmas, based mainly on water/rock (w/r), temperature (T), and the length of time (t) that fluid-rock interaction proceeds: (Type 1) epizonal systems with a wide variation in whole-rock δ18O and extreme 18O/16O disequilibrium among coexisting minerals (e.g. quartz and feldspar); (Type 2) deeper-seated and (or) longer-lived systems, also with a wide spectrum of whole-rock δ18O, but with equilibrated 18O/16O ratios among coexisting minerals; (Type 3) thoroughly homogenised and equilibrated systems with relatively uniform δ18O in all lithologies. Low-18O magmas formed by melting of rocks altered in a Type 2 or a Type 3 meteoric-hydrothermal system are the only kinds of “hydrothermal-anatectic” granitic magmas that are readily recognisable in the geological record. Analogous effects produced by other kinds of aqueous fluids may, however, be quite common, particularly in areas of extensional tectonics and large-scale rifting. The greatly enhanced permeabilities in such fractured terranes make possible the deep convective circulation of ground waters and sedimentary pore fluids. The nature and origin of low-18O magmas in the Yellowstone volcanic field and the Seychelles Islands are briefly reviewed in light of these concepts, as is the development of high-D, peraluminous magmas in the Hercynian of the Pyrenees.



2021 ◽  
Author(s):  
Mohsen Hadianpour ◽  
Ehsan Rezayat ◽  
Mohammad-Reza Dehaqani

Abstract Due to the significantly drastic progress and improvement in neurophysiological recording technologies, neuroscientists have faced various complexities dealing with unstructured large-scale neural data. In the neuroscience community, these complexities could create serious bottlenecks in storing, sharing, and processing neural datasets. In this article, we developed a distributed high-performance computing (HPC) framework called `Big neuronal data framework' (BNDF), to overcome these complexities. BNDF is based on open-source big data frameworks, Hadoop and Spark providing a flexible and scalable structure. We examined BNDF on three different large-scale electrophysiological recording datasets from nonhuman primate’s brains. Our results exhibited faster runtimes with scalability due to the distributed nature of BNDF. We compared BNDF results to a widely used platform like MATLAB in an equitable computational resource. Compared with other similar methods, using BNDF provides more than five times faster performance in spike sorting as a usual neuroscience application.



2016 ◽  
Author(s):  
George Dimitriadis ◽  
Joana Neto ◽  
Adam R. Kampff

AbstractElectrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.



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