dataesr/scientific-paragraphs-categorization huggingface dataset:
g. by conversion to NEURON) and the results displayed within the browser. We have been actively converting published cortical models to Neu-roML format and making these available on OSB. These range from point neuron models  [2, 3] , to abstract  [4]  and detailed  [5]  multicompartmental models. We are working to develop these and others into a curated set of cortical models in a common format which can be used as the basis for new models. We have also developed frameworks for importing resources from neuroinformatics datasets such as the Allen Institute Cell Types database (http://celltypes. brain-map. org) and Neu-roMorpho. org (http://neuromorpho. org). We have greatly improved compatibility between PyNN and NeuroML, allowing the modeller freedom to choose between procedural (Python) and declarative (XML) model specification.
We have also extended a model optimisation framework (https://github. com/NeuralEnsemble/NeuroTune) facilitating generation of new NeuroML models from electrophysiological data. All of this work is aimed at making existing cortical models easier to access, visualise and simulate, simplifying development of new models based on these prototypes, and ensuring the latest experimental datasets can be used to constrain and validate complex models of cortical function.
The estimated psychometric functions (solid lines) quickly approach the true functions (dashed lines) through the adaptive and optimal choice of stimuli the posterior  [5] , and show that failure to incorporate lapse rates can have deleterious effects on the accuracy of inferred parameters under both methods. We test our method on simulated data, as well as on an experimental dataset concerning the multiple-alternative choice behavior of monkeys  [6] , demonstrating that active sampling of the stimulus space facilitates the learning of the psychometric function significantly, as well as suggesting that the full range of the multidimensional stimulus could have been exploited more efficiently using our active learning framework. Finally, we discuss the comparative advantages and disadvantages of the different methods, and how one might adapt these algorithms to achieve best results.
P82 Computational investigation of energy landscapes in the resting state subcortical brain network In the present study, we investigated the energy landscape and its robustness of the subcortical human brain network that plays a central role in the human brain. The subcortical brain regions we examined were 15 regions of interests (ROIs); hippocampus, amygdala, caudate, putamen, pallidum, thalamus, nucleus accumbens, and brainstem. To construct a pairwise MEM for spontaneous interactions among subcortical brain regions, we used resting state fMRI (rs-fMRI) data of the human connectome projects, which contains 468 people's data. The blood oxygen level-dependent signals in the ROI were first binarized to represent states (zero for inactive, one for active states) of the ROI, and thereby 2 15 brain states were considered.
This allows the straightforward construction of LIF networks that sample from previously defined probability distributions. When the parameters of the distribution are not well-defined, they need to be learned from data. Due to their analogy to classical neural networks such as Boltzmann machines, LIF networks are amenable to the same learning algorithms and can be shown to match the performance of their equally-sized abstract counterparts when trained on classical machinelearning datasets such as MNIST. However, spiking neural networks endowed with short-term plasticity can travel more efficiently through their associated state space, allowing them to simultaneously become good generative and discriminative models of learned data, which is notoriously difficult with conventional techniques such as Gibbs sampling.
In order to further develop understanding of motor cortex involvement in this disease and to look at potential drug cocktails (multitarget polypharmacy), we developed a multiscale model of M1 across spatial scales, ranging from molecular interactions, up to cellular and network levels. The model contains 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics  [1, 2]. Wiring and arrangements of cellular layers of the model was based on previously recorded electrophysiological data obtained from mouse M1 circuit mapping experiments. Simulations were run in the NEURON simulator and intracellular dynamics utilized the reaction-diffusion module  [3]. The chemophysiological component of the simulation focused on calcium (Ca) handling, and Ca regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels.
P105 Using the high-level network specifications, NetPyNE instantiates the full model (all cells and connections) as a hierarchical Python structure including the NEURON objects necessary for simulation. Based on a set of simulation options (e. g. duration, integration step), NetPyNE runs the model in parallel using MPI, eliminating the burdensome task of manually distributing the workload and gathering data across computing nodes. Optionally NetPyNE plots output data, such as spike raster plots, LFP power spectra, connectivity matrix, or intrinsic timevarying variables (e. g. voltage) of any subset of cells. To facilitate data sharing, the package saves and loads the high-level specifications, instantiated network, and simulation results using common file formats (Pickle, Matlab, JSON or HDF5).
NetPyNE can convert instantiated networks to and from NeuroML, a standard data format for exchanging models in computational neuroscience. NetPyNE has been used to develop a variety of multiscale models: primary motor cortex with cortical depth-dependent connectivity; the claustrum; and sensorimotor cortex that learns to control a virtual arm. The package is open source, easily installed, and includes comprehensive online documentation, a step-by-step tutorial and example networks (www. neurosimlab. org/netpyne). We believe this tool will strengthen the neuroscience community and encourage collaborations between experimentalists and modelers. Acknowledgements: Research supported by NIH grant U01 EB017695 and DARPA grant N66001-10-C-2008.
66  Recording data (A) and simulation results (B) of SLE suppression effect by electrical stimulation  [5, 6]. Each HDC was modeled to spike at specific preferred directions separated by 60°, BVCs were modeled to spike at boundaries of cardinal directions and motor neurons were modeled to spike with Gaussian white noise as a background noise. We also built a virtual rat that navigated within a 1 m × 1 m environment whose trajectory was controlled by spikes of motor neurons receiving synaptic inputs from: (1) HDCs, (2) BVCs and (3) both HDCs and BVCs. Number of obstacle detection (detection number: DN) and the time spent during obstacle collision (collision time: CT) were analyzed to compare the efficiency of neural network for OA. We first verified that our neuromorphic HDC and BVC models could mimic the experimentally recorded electrophysiological properties in vitro and in vivo: HDCs reached maximum firing rate at each preferred direction, and BVCs increased their firing rate as the virtual rat approached boundaries.
Hence, we investigated the role of Hebbian and anti-Hebbian STDP in spike pattern propagation using a six-layered FFN model composed of 200 Hodgkin-Huxley excitatory neurons in each layer. Asymmetric and symmetric/Hebbian and anti-Hebbian STDP were modeled at excitatory synapses using exponential functions  [5]. In vivo spike train obtained from public database (crcns. org) was used as an input spike pattern (T IN ) in layer 1, which was simulated in a small subset of excitatory neurons in layer.
The EEG data were acquired using the V-Amp amplifier (Brain Products GmbH, Gilching, Germany) with 16 wet electrodes that were placed on the scalp following the international 10-20 system. Twenty one subjects participated in the study (7 female and mean age of 22. 4 years). During the experiment, a mechanical stimulus, a laser stimulus and a heat stimulus were given in a random order to subjects sixty times per stimulus. Subjects described the feeling of laser stimulation as non-painful sensation, painful sensation and no sensation. As described in the previous study, 56. 3, 12. 3 and 31. 4 % of the subjects reported laser stimulation as non-painful, painful and no sensation, respectively  [1]. To examine similarity of cortical activity in response to different stimuli, we employed a decoding analysis of the EEG data.
There has been a growing interest in the EEG-based biometric system as an alternative approach to personal authentication (PA). In this study, we focused on the potentialities of functional connectivity, especially phase synchronization in the alpha rhythm represented by the phase locking value (PLV) as a novel EEG signature for PA. We analyzed an EEG dataset of 39 trials from 7 subjects who participated in the 5-7 sessions repeatedly on different days. In the sessions, a total of 16 EEG signals were acquired by a portable EEG device, when the subjects were in a resting state with their eyes closed for 2 min. The (2) We randomly selected 20 2-s time segments of the alpha rhythm and calculated the mean phase coherence  [1]  between channels within each time segment. From all possible pairs of 16 EEG channels, a total of 120 mean alpha phase coherence values were extracted.
Our implementation uses source localized activation patterns extracted from event related epochs to classify cognitive states corresponding to working memory tasks. Subject-wise MEG data is mapped to segmented morphology from magnetic resonance imaging for source localization, then preprocessed to optimize neural network performance. The following steps minimize dimensionality and accommodate the deeplearning system's input requirements. First, spatiotemporal data are encoded using a wavelet transformation that extracts oscillatory data in the theta, alpha and beta/gamma bands per parcel (Fig. 78A ). Next, synchronicity between parcels is calculated to populate 2D connectivity matrices respective to the frequency bands. These matrices are normalized and combined into frame images where theta, alpha and beta/ gamma synchronicity is encoded as blue, green and red intensity values respectively.
These pixel grids are smoothed and expanded using gridded, cubic interpolation (Fig. 78B ). The deep learning system consists of a recursive neural network utilizing a long-short term memory (LSTM) architecture  [2]  that preserves temporal input characteristics (Fig. 78C ). LSTM presents dynamically changing oscillatory patterns to the deep learning system by integrating features of a specified range of contiguous frames relative to each training frame. As this classification system allows for visualization of activation at each layer, we are able to identify specific patterns that mediate the classification process (Fig. 78D )  [3]. Using the simulated MRI images provided the BrainWeb database  [1] , the performance of the segmentation was tested. The performance shown in Fig. 79  indicates that the multiscale complexity analysis is very robust against noise.
We find that directionality plays a major role in shaping the brain networks. All regions are affected, including hub nodes, which have large degree and enhanced importance in information integration for cognitive functions  [5]. We compute and rank graph theoretical measures and determine their resilience with respect to the loss of directionality of the network. Overall, our results suggest that the characterization of connectomes can be compromised in the absence of data regarding the directionality of brain networks. Randomly connected networks of rate-model neurons have a rich dynamics  [1] , a feature that has been exploited to model a variety of phenomena  [2]. These model networks typically do not distinguish between excitatory and inhibitory neuron classes. Doing this requires constraining the network connectivity matrix to have columns with exclusively positive entries, representing input from excitatory neurons, and with negative entries, representing input from inhibitory neurons.
Analysis of fMRI data typically focuses on inter-regional functional connectivity, measured as pairwise correlations, or through multivariate decompositions (e. g. , ICA). Relatively little attention is given to the univariate time-series properties of BOLD signals within a specific brain region, despite a broad scientific literature on time-series analysis (including power spectral techniques, information theoretic methods, model fitting, nonlinear time-series analysis, and fractal scaling). Here we undertake the largest systematic comparison of over 7000 such measures of temporal structure to identify the temporal features of individual BOLD signals, and their locations in the brain, that are most discriminative of people with schizophrenia. MRI data were obtained from the open COBRE database  [4]  for 72 people with schizophrenia (SCZ) and 74 healthy controls (CON).
For each subject, we extracted 7779 temporal features from the BOLD time series recorded in each of 264 brain regions using the publicly available highly comparative time-series analysis framework, hctsa (http:// benfulcher. github. io/hctsa/) [1]. Spatial analysis ROIs that were most discriminative of SCZ versus CON were identified by training a separate linear support vector machine (SVM) classifier for each ROI, across all features, using tenfold cross validation. We identified 23 ROIs with a classification accuracy exceeding chance levels (p < 0. 05, FDR-corrected) with some individual ROI accuracies reaching 69. 5 %. These discriminative brain regions were mostly located in the frontal and parietal cortices. Temporal analysis The most discriminative temporal features were deduced using t-tests in each ROI, and then averaging across all ROIs.
Our model is publicly accessible in ModelDB, accession number 187474, as NEURON and NeuroML-2 descriptions (https://senselab. med. yale. edu/ModelDB/showModel. cshtml?mo del=187474). A recent genome-wide association study (GWAS) of schizophrenia (SCZ) has identified more than a hundred genetic loci exceeding genome-wide significance, confirming the polygenic nature of the disorder  [1]. The loci implicate genes that encode numerous ion channel subtypes and calcium transporters, and are major contributors not only to the function of brain cells, but also to the functioning of organs outside the central nervous system, such as heart. Meta-studies have reported a 2. 5-fold-threefold increase in mortality rates in schizophrenic patients, and majority of these excess deaths are natural and mostly due to cardiovascular disease  [2].
We here present a multi-scale spiking network model of all visionrelated areas of macaque cortex that represents each area by a fullscale microcircuit with area-specific architecture based on a model of early sensory cortex  [1]. The layer-and population-resolved network connectivity integrates axonal tracing data from the CoCoMac database with recent quantitative tracing data, and is systematically refined using dynamical constraints  [2]. Gaps in the data are bridged by exploiting regularities of cortical structure such as the exponential decay of connection densities with inter-areal distance and a fit of laminar patterns versus logarithmized ratios of neuron densities. Simulations reveal a stable asynchronous irregular ground state with heterogeneous activity across areas, layers, and populations.
In the proposed NN model, the hidden layer is coupled to a vascular network in a one-to-one fashion (Fig. 104A ) and is trained using backpropagation. The cross-entropy (ce) error measure is used to update the energy demand parameter (M d ) which is fed back to the VN. M d in turn governs the state of the vascular units, which determines if the neuron should be turned ON/OFF. This paradigm of randomly turning neural units ON/OFF is adapted from  [4]. High M d results in an increase in ce as the neuronal dropout level is too low; similarly low M d results in an increase in ce due to high dropout. The time scale of vascular dynamics (M d ) is much longer than that of neural dynamics (ce), reflecting physiology. The network was trained on two datasets: overlapping bar patterns and MNIST data.
Acknowledgements: Supported by  NSERC of Canada , an  Ontario Graduate Scholarship , and the  SciNet HPC Consortium. Acknowledgements:  Polish Ministry for Science and Higher Education  (grant  2948/7. PR/2013/2 ),  Hungarian Scientific Research Fund  (Grant  OTKA K113147 ),  National Science Centre, Poland  (Grant  2015/17/B/ST7/04123 ). Acknowledgements: This work was funded by the  National Institutes of Health , through the joint  National Science Foundation/National Institute of General Medical Sciences Mathematical Biology Program  grant No. R01GM104974  (to M. R. B. and K. J. ), National Science Foundation grants Nos. DMS-1311755  (to Z. P. K. ) and  DMS-1122094  (to K. J. ), the  Robert A. Welch Foundation  grant No. C-1729  (to M. R. B. ),  National Science Foundation  grant No. DMS-0931642  to the  Mathematical Biosciences Institute  (to J.
Acknowledgements: We acknowledge support from  MINECO   DPI2015-65833-P ,  TIN2014-54580-R, TIN-2012-30883  and  ONRG  grant  N62909-14-1-N279. Acknowledgements: This research was supported by the  Department of Veterans Affairs , the  Korean National Research Council of Science & Technology  (No. CRC-15-04-KIST ),  NIMH   R01 MH039683  and  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( 2015R1D1A1A01059119 ). The contents of this report do not represent the views of the  US Department of Veterans Affairs  or the United States government. Acknowledgements:  Dr. Yu  thanks for the support from the  National Natural Science Foundation of China  ( 31271170 ,  31571070 ),  Shanghai program  of  Professor of Special Appointment (Eastern Scholar   SHH1140004 ).
Acknowledgements: The work of JB, RG, and SMC was supported in part by  R01MH1006674  from the  National Institutes of Health. Acknowledgements: The work was supported by the  NSF   1422438  grant and by the  Houston Bioinformatics Endowment Fund. Acknowledgements: This research was partially supported by the  Japan Science and Technology Agency (JST)  under the  Strategic Promotion of Innovative Research and Development Program. Acknowledgement: This work was supported by  Bundesministerium für Bildung und Forschung  Grant  01GQ1001A  and  DFG  Grant  609788-L1 1046/2-1. Acknowledgement: This work was supported in part by  JSPS KAK-ENHI  Grant Numbers  21700121 ,  25280110 , and  25540159. Acknowledgements: We are grateful to  Shigeyoshi Fujisawa  and  Michael J Berry  for sharing us the cortical and retinal data, respectively.
We also thank  Hideaki Shimazaki  and  Taro Toyoizumi  for stimulating discussions. This work was supported by the program for  Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/ MINDS ) from  Japan Agency for Medical Research and development, AMED. Acknowledgements: JPR was supported by an  NSF   Graduate Research Fel-lowship Program  under Grant No. DGE 1256260  and a  UM Rackham Merit Fellowship. MRZ and LMS were supported by  NSF   PoLS 1058034. Acknowledgements: This work was partially supported by  Hong Kong Baptist University Strategic Development Fund ,  NSFCRGC Joint Research Scheme HKUST/NSFC/12-13/01  (or N-HKUST  606/12 ), RGC (Grants No. 604512 , No. 605813 , and No. 12302914 ),  NSFC  (Grants No. 11275027 , No. 11328501 , and No. 11305098 ), and the  Fundamental Research Funds for the Central Universities  (Grant No.
GK201302008 ). Acknowledgements: This work was partially funded by the  Brazilian agency CNPq  (Grant Agreement Number  235144/2014-2 ) and the  Sir Henry Dale Fellowship (Grant Agreement   WT100000 ). Acknowledgements: Research was supported by a  Sir Henry Dale Royal Society  and  Wellcome Trust   Research Fellowship  ( WT100000 ). Acknowledgements: Supported by  EC Fp7-PEOPLE-2011-ITN PITN-GA-011-290011 (ABC) ,  FP7-ICT-2013-FET-F/604102  (HBP),  FP7-ICT-2013-10/611909  (Koroibot), BMBF, FKZ:  01GQ1002A ,  DFG GI 305/4-1 + KA 1258/15-1. Acknowledgements: Research was supported by a  Sir Henry Dale Royal Society & Wellcome Trust   Research Fellowship  ( WT100000 ). A. S. was supported by the  Swiss National Science Foundation  ( 200020_147200 ). R. R. was supported by the  EPFL Blue Brain Project Fund and the ETH Board  funding to the  Blue Brain Project.
Acknowledgements: We acknowledge support from  MINECO   FIS2013-43201-P ,  DPI2015-65833-P, TIN-2012-30883  and  ONRG  Grant  N62909-14-1-N279. Acknowledgements: This work was supported by funding from the  EPSRC. Acknowledgements: Work supported by  GAČR   14-16755S ,  GAUK 396213 ,  MŠMT 7AMB12FR002 ,  NIH   1RO1DCO12441  and  ANR 2010-BLAN-1401-01. Acknowledgements: This work was supported by the  Office of Naval Research. Acknowledgements: This work was supported by the  Office of Naval Research. Acknowledgements: This work was supported by the  BMBF  (FKZ:  01GQ1001A ) and the  DFG  (research training group  GRK1589/2 ). Acknowledgements: Supported by  NSERC Canada, U. of T. Fellowship P. S. L. , and the  SciNet HPC Consortium. Acknowledgement: This research is funded by the  Wellcome Trust.
Acknowledgements: This work has been primarily funded by the  Wellcome Trust  ( 101445/095667 ). Acknowledgements: This research was supported by the  Washington Research Foundation Innovation Postdoctoral Fellowship in Neuroengineering (HC) ,  National Science Foundation CRCNS  Grant  IIS1309725  (AP), and  NEI  Grant  R01EY018839  (AP). Acknowledgements: Supported by  HHMI. Acknowledgements: I thank  Barclay Lee  and  Dae-Hyun Kim  for their assistance. This work was supported by the research fund of the  KAIST (Korea Advanced Institute of Science and Technology)  (Grant code:  G04150045 ). Acknowledgements: The study was supported by  ICTSI  and the  Indiana University Health -Indiana University School of Medicine Strategic Research Initiative. Acknowledgements: AL received funding from the  Marie Curie Actions  (FP7/2007-2013) under REA Grant Agreement No.
Acknowledgements: We wish to thank the  Allen Institute founders ,  Paul G. Allen  and  Jody Allen , for their vision, encouragement and support. Acknowledgements: This work was supported by funding from the  ETH Domain for the Blue Brain Project (BBP). The BlueBrain IV IBM BlueGene/Q system is financed by  ETH Board Funding  to the  Blue Brain Project  and hosted at the  Swiss National Supercomputing Center (CSCS). We thank  M. Bale  and  R. Petersen  for providing the VPM spike trains. Acknowledgements: Research supported by  NIH  grant  R01 MH086638 ,  NIH  grant  U01 EB017695 ,  NIH  grant  R01 NS064046 ,  NIH  grant  R01 DC012947. Acknowledgements: Supported by  National Research Foundation of Korea ,  NRF-2013R1A2A2A03017022. Acknowledgements: This research was supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT and Future Planning  ( NRF-2014R1A2A1A11051982 ).
Acknowledgements: This research was supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT & Future Planning  ( 2015R1C1A1A01052438  to  C. Park  and  2014R1A2A1A11052103  to  H. W. Lee ), and by the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI)  funded by the  Ministry of Health & Welfare  ( HI14C1989  to  H. W. Lee ). Acknowledgements: This work was supported by the  National Research Foundation of Korea  (No. 2014R1A2A1A11052763 ). Acknowledgements: This study was supported by the  Basic Science Research Program  through the  National Research Foundation of Korea  funded by the  Ministry of Science, ICT and Future Planning  ( NRF-2013R1A1A2053280 ).