dataesr/scientific-paragraphs-categorization huggingface dataset:
This enables in-depth exploration and investigation of the structure of brain circuits, using intuitive natural language queries that are capable of revealing latent structure and information. Equipped with powerful 3D visualization, NeuroNLP standardizes tools and methods for graphical rendering, representation, and manipulation of brain circuits, while integrating with existing databases such as the FlyCircuit. It currently supports queries to show, add, filter and remove neurons based on 1) the parent neuropil, 2) neuron type (local or projection), 3) dendritic/ axonal arborization, 4) neurotransmitter and 5) related postsynaptic or presynaptic neurons. The graphical user interface complements the natural language queries with additional controls for exploring neural circuits. Designed with an open-source, modular structure, it is highly scalable and extensible to additional databases and languages.
We used datasets of 3000 patterns divided into 10 pattern classes and 3 concentration levels. To model the intrinsic variations observed in real olfactory systems, we added a multiplicative white noise to these Gaussians with 3 different levels (small, medium, large). The performance of a network with this gain control mechanism presented significantly lower classification error rate than a network without gain control, with an improvement of ~45%. A network with this gain control achieved a classification error of ~0% for sets of patterns with small and medium noise and <5% for large noise. These results suggest that gain control mechanism does not only suppress outbursts of activity from input layers but also greatly improves learning in Mushroom Bodies. Finally, because this mechanism does not depend on any synaptic plasticity, in agreement with the biological literature, it can also be applied to chemical sensors in electronic devices for controlling changes in environmental conditions  [6, 7].
We call max R AUC relative since it is a maximum value within each trial, not the maximum value of all trials. This method calculates max R AUC incrementally in time for each stimulus. The one with the highest value is considered a target stimulus. In this way, the differences between a target and a non-target stimulus are maximized. Electrodes having the highest max R AUC in the ERP region of the signal are potentially likely to have better characteristics for detecting ERP effectively. Our method was tested with a linear classifier (LDA) based on the Krusienski method (KM)  [1]  and the dataset_IIb of the BCI competition (http://www. bbci. de/competition/ii/). This dataset contains the data of one user, divided into three sessions: two training sessions (called 10 and 11) and one session to test the classifier.
P227 The other is to model a spike as an intrinsically stochastic event. The second way can be implemented in two different but equivalent manners: by a randomly varying spike threshold as in the escape noise model  [4] , or by a spike probability function Φ(V), which depends on the membrane potential V as in the simplified version of the Galves-Löcherbach (GL) discrete-time model  [5]  recently proposed by Brochini et al. [3]. Here we have considered the Brochini et al. [3]  version of the GL model (from here onwards simply called GL model) and empirically determined the probability function Φ(V) so that the model can describe stochastic firing behaviors of the two most import cortical cell types, namely regular (RS) and fast (FS) spiking neurons  [6]. To determine Φ(V) for these two cell types, biophysically detailed models of RS and FS neurons were chosen from the neuron database ModelDB (http:// senselab.
After extending the network model to all core motor regions and fitting the model parameters to the experimental data it will serve as a tool to make predictions on disturbed networks dynamics, e. g. decoupling of nodes. In this work, we present a low cost embedded system to simulate Spiking Neural Networks through PyNN  [1]. PyNN is a Python library widely used in the neuroscience community to simulate at software and hardware level several existent simulators (NEURON, NEST, PCSIM and BRIAN) by acting as an interface to unify the different instructions and neuron model definitions. At hardware level, serves as a high-level interface to directly map spiking neuron models on the SpiNNaker neuromorphic system  [2]. Albeit, SpiNNaker and other systems such as TrueNorth have demonstrated tremendous capabilities to process information such as the brain does, these systems are still unreachable for the large community who wants to implement or validate simplest models on a hardware platform.
We found that intervals of strong bilateral coherence, lasting for up to a dozen seconds, alternate with time intervals of insignificant coherence. We also observed intermittent bilateral coherence for physiological tremor (a normal hand oscillation of low amplitude) recorded in healthy subjects. Here we further extend the analysis of Ref. [5] , based on the same dataset of accelerometric recordings obtained from 34 ET patients and 42 healthy subjects. We analyze the distribution of durations of the bilaterally coherent time intervals extracted from wavelet analysis, and examine its dependence on the tremor type (physiological vs. essential) and on the hand position. The statistical significance of the Viewing of ambiguous stimuli can lead to bistable perception alternating between the possible percepts.
The score enables the plotting of spike locations, color coded with the local grid score or the local orientation of the grid and could thus simplify the visualization of experimental data. More specifically, it could be used to quantify and highlight recent experimental findings, like boundary effects on the structure of grids in asymmetric enclosures  [5] , drifts in grid orientation along the arena  [6]  or the preferred alignment of grids to one of the boundaries  [6]. The grid score is applicable to any n-fold symmetry. We provide a public Python package (using SciPy and NumPy) that efficiently determines the grid score directly from spike locations.
All of the possible phase-to-phase interactions between the oscillators of the network are inferred. Thus, the coupling can be decomposed into its partial functional contributions  [8]. This allows one e. g. to isolate the estimated direct coupling between two nodes from the possible common coupling and self-coupling also involved in the interaction. As an illustrative example, the method is applied to characterization of the phase-to-phase neural coupling functions in electroencephalographic (EEG) data from the Neurophysiological Biomarker Toolbox (NBT) dataset  [9]. Comparisons are made between the resting states with the eyes open (EO) and eyes closed (EC). We constructed the network by investigating the couplings between delta and alpha waves extracted from any pair of probes within the 10-20 measuring system; and we used phase-shuffled surrogates to test the significance of the inferred direct coupling strength.
Several techniques can evidence sets of variables which provide information for the future state of the target. This information can be synergetic or redundant, with important implication on our understanding of the functioning of the dynamical system under analysis. Importantly, not taking into account the joint dynamical influence of two or more variables can lead to bias and wrong estimations of links (false positive and false negatives). In the field of information theory these concepts are often defined and studied by means of axioms. Here we will instead use an operative definition based on reduction in variance, using the unnormalized version of Granger causality. We will present an application to simulated datasets and neuroimaging data, such as the one depicted in Figure  1 , where average redundant and synergetic contributions, computed on 116 brain regions from 90 subjects from the Human Connectome Project dataset are depicted.
The network contains 8000 neurons and more than 10 6 synapses, based on connectivity data from cat primary visual cortex. The sensory stimulus is modeled as 7 to 50 Hz square or sine modulated rate of Poisson process. The simulated EEG signal is as a sum of synaptic currents of all pyramidal neurons. Additionally, for the square stimulus, we varied duty cycle: 50% (default), 33% and 66% in order to investigate whether SSEP spectral power depends on magnitude of ON and OFF responses or on the overall energy of the stimulus. The magnitude of transient responses was determined by firing adaptation strength of excitatory cells by modulating the conductivity of Cadependent potassium current. We compare the simulation data with experimental EEG recordings obtained in somatosensory cortex during vibrotactile stimulation as well as from visual cortex in response to flickering stimuli.
Conclusion: Overall, our results point to a dynamical signature for representations of cortical activity whereby states near the critical point are most amenable to decoding by linear downstream structures. Computation in the brain may occur by distributed processing whereby several subnetworks are each responsible for contributing to dynamical neuronal representations. Neuronal reconstructions are essential building blocks for neuronal tissue simulation at subcellular resolution. Several databases with thousands of reconstructed morphologies exist, the largest being NeuroMorpho. Org  [1]. In this database, the location of each neuron is described in terms of species and brain region. For a neuronal tissue builder like the one created in the Human Brain Project (HBP), this information is too coarse.
The Neurolucida file (choice of binary, asci, xml) is read by a newly written open-source parser (https://www. npmjs. com/ package/morphology_io), and converted to a newly developed SWC + format (https://github. com/HumanBrainProject/swc-Plus), an extension of the widely used SWC format  [3]. 3. The markers with atlas coordinates are extracted from the SWC + file by a python script and used to estimate an affine transformation that maps the local coordinate system to the reference space. The transformation parameters are saved to the SWC + file. 4. The Morphology Viewer  [4] , a new web-based morphology suite, recognizes the transformation parameters in the file, and displays the neuron along with sections from the atlas, see screenshot in Fig. 1. In the use-case, 50 points were manually mapped to the atlas.
These models provide a novel way to explore how individual neuron and network properties contribute to functional pathology of the striatal microcircuit in Q175 mice, in order to better understand the deleterious effects of mutant Huntingtin in the human brain. A similar study has been performed in  [1] , but here we study the problem in greater depth and on a much larger dataset. We analyze 30-minute EEG recordings of 93 patients with epilepsy. The data was recorded according to the standard 10-20 electrode placement system at Massachusetts General Hospital (MGH), Boston. IEDs were independently annotated by two CNs. Each IED was extracted as a 500-millisecond waveform. A total of 18,164 IEDs were extracted. The CNN was developed using Tensorflow-r0. 12  [2]  with a Tesla K40 GPU. We developed the CNN model with five layers: an input layer, convolutional layer, pooling layer, fully connected layer, and output layer.
Interactions between cortical rhythms may generate a third frequency  [9]. Route reference points are additionally encoded by the amplitude of rhythms in all modes. QGIS (http://www. qgis. org) allows to integrate data received from various sources simultaneously. In the work, GPS track of flights and landscape maps are performed in QGIS (similarly, QGIS was applied in  [6] ). In addition, the program allows to combine results of EEG data processing with the spatial characteristics of pigeon flight. In the spatial representation of the model takes into account the distances between the reference points on the ground. Rhythmic activities of different frequency bands have been observed universally in the brain and are thought to play important roles in various cognitive processes  [1]. However, the fundamental mechanism of how these neural oscillations contribute to brain activities is still an open question.
The model was developed using NetPyNE, a novel Python package that facilitates the development of biological neuronal networks in the NEURON simulator, and emphasizes the incorporation of multiscale anatomical and physiological data at varying levels of detail. Parallel simulations were executed on the XSEDE San Diego Supercomputer Center. Our M1 model incorporates quantitative experimental data from 14 publications, therefore accumulating previously isolated knowledge into a unified framework. We studied the output of the 15 local M1 populations in response to increased input from each of the seven long-range inputs modeled. Sensory information from S1, S2 and sensory thalamus primarily modulated M1 superficial layers, which projected unidirectionally to layer 5B corticospinal neurons. Secondary motor cortical areas, as well as basal ganglia-relaying thalamic direct inputs, also modulated the same layer 5B circuits.
Because of this item, it is necessary to conduct a system of tasks. Brain activity which was obtained using the second way also connects not only with certain cognitive processes, but reflects primarily default state of the brain. Furthermore, another serious problem is artifacts and physiological noise  [2]. A new method of system visualization of cognitive functioning for fMRI (Russian Federation #2016149614 patent pending) and corresponding software (Fact-fMRI) for complex factor analysis for several individual datasets is presented (Fig. 1 ). Compared to the ICA method, the new method allows to estimate the number of functional systems by orthogonal factors which can be additionally rotated. This method allows to formalize identification of brain systems which are involved in executing of different cognitive tasks.
Although, inhibitory synapses are still placed on nearby somas, they generate effectively closed-field symmetry, which does not produce large far-field potentials. Recently, we have shown, however, that the electric field following a single interneuron spike dominates the on-going LFP. Excitatory neurons contribute to the LFP with longer latencies suggesting that their contribution is di-synaptic, mediated by an intermediary interneuron  [5]. In the present study, we are investigating this discrepancy using computational modelling. We place pyramidal neurons according to their distribution in the hippocampus and neocortex and activate inhibitory or excitatory synapses on them. We follow the distributions of neurons and synapses found in the literature and realistic morphology downloaded from online databases (neuromorpho.
org). The simulation of the model is performed in NEURON simulator through its Python interface and the extracellular field is calculated using the NeuroEAP library  [6]. We reproduce the findings of Bazelot et al. [4]  in the hippocampus and Telenczuk et al. [5]  in the neocortex and show that the difference in the field magnitude originate from the differences in the distribution of synaptic target of inhibitory and excitatory neurons. Importantly, we find that the magnitude of the LFP generated by the synapses and the relative contribution of excitatory vs. inhibitory pre-synaptic neurons depend on the cortical layer and the source of feedforward activation.
Acknowledgements Research supported by  NIH  grant  5R01MH086638  References Acknowledgements Research supported by  NIH   R01 MH086638. Open Access Acknowledgements This research was supported by the  Fund for Scientific Research-Flanders (FWO-V) , Grant  FWO16/ASP_H/255. Acknowledgements Supported by  EC FET Flagship   HBP SGA1  ( 720270 ) to MM and MVSV Acknowledgements Acknowledgements This work was supported by institutional funds from  Scuola Superiore Sant'Anna , by the  Italian Ministry of Health  ( GR-2009-1594645  grant), by the  Aldo Ravelli Donation for Research on Parkinson Disease , by the  Bertarelli Foundation , and by institutional funds from  École Polytechnique Federale de Lausanne. Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  720270  Acknowledgements We acknowledge support by  MINECO (Spain)  under project No.
~FIS2014-59462-P , and the project COSMOS of the European Union's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 642563. Acknowledgements The  Spanish Ministry of Economy and Competitiveness   IJCI-2014-21937  grant (to G. M. ); the  Marie Curie   FP7-PEOPLE-2010-IRG  grant  PIRG08-GA -2010-276795 , and the  Spanish Ministry of Economy and Competitiveness   PSI2013-44811-P grant  (to R. M. B. ) Acknowledgements This work was supported by  CNCS -UEFISCDI  ( PN-II-RU-TE-2014-4-0406  and  PN-III-P3-3. 6-H2020-2016-0012 ). Acknowledgements This material is based upon work supported by the  NIH  ( R01 MH1107491 ; Chafee);  NRSA F30 MH108205-01A1  (Zick);  NSF Career Award (TIN) ;  Medical Scientist Training Program   NIH T32-008244  Acknowledgements Research supported by the  Systems Neuroengineering NSF   IGERT Program  ( DGE-1069104 ),  NIH   R01-NS094206 ,  NIH   P50-NS098573 , and  NSF   CBET-1264432.
Acknowledgements This work was supported by the  Deutsche Forschungsgemeinschaft  ( GRK1589/2 ). Acknowledgements SC is recipient of a Ph. D. fellowship from  CONICYT. PO is partially funded by the  Advanced Center for Electronic Engineering  ( FB0008 CONICYT , Chile). The  Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)  is a  Millennium Institute  supported by the  Millennium Scientific Initiative of the Ministerio de Economía (Chile). Acknowledgements KX is funded by  Proyecto Fondecyt 3170342. PO is partially funded by the  Advanced Center for Electrical and Electronic Engineering  ( FB0008  Conicyt, Chile). The  Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)  is a  Millennium Institute  supported by the  Millennium  Acknowledgements We thank  A. Azizi  and  S.
Acknowledgements The joint  NIH-NSF CRCNS program  through  NSF  grant  1515686  Acknowledgements This work was supported by the  Office of Naval Research. Acknowledgements This work is partially supported by the  Ministry of Education and Science of the Russian Federation  within the research project  №3. 9499. 2017included into the basic part of research funding assigned to  Kursk State University. Acknowledgements This work was funded by  MINECO/FEDER   DPI2015-65833-P  (http:// www. mineco. gob. es/) and  ONRG  grant  N62909-14-1-N279  (PV) and by  ONR   MURI 14-13-1-0205  and  MURI N00014-13-1-0678  (MIR)  Acknowledgements Career Award at the Scientific Interface (CASI)  from the  Burroughs Wellcome Fund  awarded to AAP. Acknowledgements This material is based upon work supported by the  National Science Foundation  under Grant Number  CRCNS 1515678  Acknowledgements This work was supported by the  Australian Research Council  under  Center of Excellence for Integrative Brain Function Grant   CE140100007  and  Laureate Fellowship Grant   FL140100025.
Acknowledgements This work was funded by  Spanish projects of Ministerio de Economía y Competitividad/FEDER   TIN2014-54580-R ,  DPI2015-65833-P ,  ONRG  grant  N62909-14-1-N279 ,  Spanish-Brazilian Cooperation   PHB2007-0008  and 7ª  Convocatoria De PROYECTOS de COOPERACION INTERUNIVERSITARIAUAM-SANTANDER con America Latina and Brazilian Agency of Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado de São Paulo.
Acknowledgements This work was produced as part of the activities of  FAPESP Research, Disseminations and Innovation Center for Neuromathematics  (grant  2013/07699-0 ,  S. Paulo Research Foundation ). NLK is supported by a  FAPESP postdoctoral fellowship  (grant  2016/03855-5 ). ACR is partially supported by a  CNPq fellowship  (grant  306251/2014-0 ).
Acknowledgements Supported by  Czech Science Foundation  ( P304/12/G069 ),  Charles University in Prague  ( Progres Q27, SVV NeST III ), and  Czech Health Research Council  ( AZV 16-28119A ). Acknowledgements This work was supported by the  Priority Program 1665 of the DFG  ( DU 1433/1-1  to SD and JR, and  SCHN 1370/2-1  to MG and GS), by an  EMBO  long-term fellowship ( ALTF_210-2012  to SD), and by the  German Federal Ministry of Education and Research (BMBF ,  01ZX1404B  to GS). Acknowledgements This research has been supported by the  CONACYT  project " Aplicación de la Neurociencia Computacional en el Desarrollo de Sistemas Robóticos Biológicamente Inspirados" (No 269798 ). Acknowledgements This work was supported by the  German Federal Ministry of Education and Research (BMBF , Funding number:  01ZX1404B ; SA, KS, GS).
Acknowledgements This work was supported by the  Einstein Foundation Berlin  and the  German Federal Ministry of Education and Research  ( 01GQ0901 ,  01GQ1403 ). Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  720270. Acknowledgments This research was supported by  ANR  - Essilor  SilverSight Chair  ANR-14-CHIN-0001  Acknowledgements yFunded by the  Wellcome Trust  and the  ERC.
Acknowledgements This work was supported by the  Engineering and Physical Sciences Research Council (UK)  [Grant No. EP/100999X1 ], by the  EU projects BRACCIA  [517133] and COSMOS [642563], and by the  Action Medical Research (UK)  project  MASDA  [GN1963]. VT is supported by a PhD grant from the  Department of Physics, Lancaster University. Our grateful thanks are due to  Klaus Lehnertz  and  Andreas Daffertshofer  for valuable discussions. We also thank  Lall Hussain  for pointing out the toolbox and for his initial contribution to the work, and  Lars Michels  and the  NBT  research team for sharing their data and for most useful comments.
Acknowledgements Helmholtz Portfolio Supercomputing  and  Modeling for the Human Brain (SMHB) ,  European Union  (BrainScaleS, grant  269921  and  Human Brain Project , grant  604102 ), the  Jülich Aachen Research Alliance (JARA) , and the  German Research Council  ( DFG grants   SFB936/A1 , Z1  and  TRR169/A2 ).
Acknowledgements This work was supported by the  Polish National Science Center  (project  EvoSN ,  UMO-2013/08/M/ST6/00922 ). Acknowledgements Supported by  NSF   1458840  (A. M. , S. S. , K. Y);  NSF   1458495 ,  NIH/NIDCD DC 009977 ,  NIH/NINDS   NS 011613 (T. C ).
The analysis include the use of different structure from motion (SfM) programs and a comparison between the extracted photogrammetric point clouds and a lidar-derived mesh that was used as a ground truth. Results show that both landslide deformation and estimation of fallen volumes were clearly identified in the different point clouds. Results are site-and softwaredependent, as a function of the image set and number of images, with model accuracies ranging between 0. 2 and 3. 8 m in the best and worst scenario, respectively. Although some limitations derived from the generation of 3-D models from SVI were observed, this approach allowed us to obtain preliminary 3-D models of an area without on-field images, allowing extraction of the pre-failure topography that would not be available otherwise.