scholarly journals Detecting Synfire Chain Activity Using Massively Parallel Spike Train Recording

2008 ◽  
Vol 100 (4) ◽  
pp. 2165-2176 ◽  
Author(s):  
Sven Schrader ◽  
Sonja Grün ◽  
Markus Diesmann ◽  
George L. Gerstein
Keyword(s):  
Single Unit ◽  
Large Scale ◽  
High Reliability ◽  
Large Data ◽  
Recurrent Network ◽  
Neuronal Firing ◽  
Chain Model ◽  
Synfire Chain ◽  
Synfire Chains ◽  
Single Unit Recordings

The synfire chain model has been proposed as the substrate that underlies computational processes in the brain and has received extensive theoretical study. In this model cortical tissue is composed of a superposition of feedforward subnetworks (chains) each capable of transmitting packets of synchronized spikes with high reliability. Computations are then carried out by interactions of these chains. Experimental evidence for synfire chains has so far been limited to inference from detection of a few repeating spatiotemporal neuronal firing patterns in multiple single-unit recordings. Demonstration that such patterns actually come from synfire activity would require finding a meta organization among many detected patterns, as yet an untried approach. In contrast we present here a new method that directly visualizes the repetitive occurrence of synfire activity even in very large data sets of multiple single-unit recordings. We achieve reliability and sensitivity by appropriately averaging over neuron space (identities) and time. We test the method with data from a large-scale balanced recurrent network simulation containing 50 randomly activated synfire chains. The sensitivity is high enough to detect synfire chain activity in simultaneous single-unit recordings of 100 to 200 neurons from such data, enabling application to experimental data in the near future.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

Sources of the Scalp-Recorded Amplitude-Modulation Following Response

2002 ◽  
Vol 13 (04) ◽  
pp. 188-204 ◽  
Author(s):  
Shigeyuki Kuwada ◽  
Julia S. Anderson ◽  
Ranjan Batra ◽  
Douglas C. Fitzpatrick ◽  
Natacha Teissier ◽  
...  
Keyword(s):  
Animal Model ◽  
Amplitude Modulation ◽  
Single Unit ◽  
Modulation Frequency ◽  
Multiple Sources ◽  
High Modulation ◽  
Before And After ◽  
Single Unit Recordings ◽  
Composite Response ◽  
The Brain

The scalp-recorded amplitude-modulation following response (AMFR)” is gaining recognition as an objective audiometric tool, but little is known about the neural sources that underlie this potential. We hypothesized, based on our human studies and single-unit recordings in animals, that the scalp-recorded AMFR reflects the interaction of multiple sources. We tested this hypothesis using an animal model, the unanesthetized rabbit. We compared AMFRs recorded from the surface of the brain at different locations and before and after the administration of agents likely to enhance or suppress neural generators. We also recorded AMFRs locally at several stations along the auditory neuraxis. We conclude that the surface-recorded AMFR is indeed a composite response from multiple brain generators. Although the response at any modulation frequency can reflect the activity of more than one generator, the AMFRs to low and high modulation frequencies appear to reflect a strong contribution from cortical and subcortical sources, respectively.

scholarly journals Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

2021 ◽  
Vol 11 (6) ◽  
pp. 761
Author(s):  
Gert Dehnen ◽  
Marcel S. Kehl ◽  
Alana Darcher ◽  
Tamara T. Müller ◽  
Jakob H. Macke ◽  
...  
Keyword(s):  
Data Quality ◽  
Single Unit ◽  
Human Subjects ◽  
External Noise ◽  
Hospital Environment ◽  
Noise Sources ◽  
Recording Channels ◽  
Waveform Similarity ◽  
Therapeutic Procedures ◽  
Single Unit Recordings

Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.

Network effect in shared supply chain platform value co-creation behavior in evolutionary game1

2021 ◽  
pp. 1-12
Author(s):  
Zou Xiaohong ◽  
Chen Jinlong ◽  
Gao Shuanping
Keyword(s):  
Boundary Condition ◽  
Supply Chain ◽  
Large Scale ◽  
Simulation Analysis ◽  
Supply And Demand ◽  
Evolutionary Game ◽  
Dynamic Game ◽  
Network Effect ◽  
Chain Model ◽  
The Cost

The shared supply chain model has provided new ideas for solving contradictions between supply and demand for large-scale standardized production by manufacturers and personalized demands of consumers. On the basis of a platform network effect perspective, this study constructs an evolutionary game model of value co-creation behavior for a shared supply chain platform and manufacturers, analyzes their evolutionary stable strategies, and uses numerical simulation analysis to further verify the model. The results revealed that the boundary condition for manufacturers to participate in value co-creation on a shared supply chain platform is that the net production cost of the manufacturers’ participation in the platform value co-creation must be less than that of nonparticipation. In addition, the boundary condition for the shared supply chain platform to actively participate in value co-creation is that the cost of the shared supply chain platform for active participation in value co-creation must be less than that of passive participation. Moreover, value co-creation behavior on the shared supply chain platform is a dynamic game interaction process between players with different benefit perceptions. Finally, the costs and benefits generated by the network effect can affect value co-creation on shared supply chain platforms.

scholarly journals A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Harshi Weerakoon ◽  
Jeremy Potriquet ◽  
Alok K. Shah ◽  
Sarah Reed ◽  
Buddhika Jayakody ◽  
...  
Keyword(s):  
T Cell ◽  
Large Scale ◽  
Large Data ◽  
Spectral Library ◽  
Proteomics Data ◽  
Cell Library ◽  
Public Resource ◽  
Human T Cell ◽  
Theoretical Mass ◽  
Current Resource

AbstractData independent analysis (DIA) exemplified by sequential window acquisition of all theoretical mass spectra (SWATH-MS) provides robust quantitative proteomics data, but the lack of a public primary human T-cell spectral library is a current resource gap. Here, we report the generation of a high-quality spectral library containing data for 4,833 distinct proteins from human T-cells across genetically unrelated donors, covering ~24% proteins of the UniProt/SwissProt reviewed human proteome. SWATH-MS analysis of 18 primary T-cell samples using the new human T-cell spectral library reliably identified and quantified 2,850 proteins at 1% false discovery rate (FDR). In comparison, the larger Pan-human spectral library identified and quantified 2,794 T-cell proteins in the same dataset. As the libraries identified an overlapping set of proteins, combining the two libraries resulted in quantification of 4,078 human T-cell proteins. Collectively, this large data archive will be a useful public resource for human T-cell proteomic studies. The human T-cell library is available at SWATHAtlas and the data are available via ProteomeXchange (PXD019446 and PXD019542) and PeptideAtlas (PASS01587).

scholarly journals Compartment and hub definitions tune metabolic networks for metabolomic interpretations

GigaScience ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
T Cameron Waller ◽  
Jordan A Berg ◽  
Alexander Lex ◽  
Brian E Chapman ◽  
Jared Rutter
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Shortest Paths ◽  
Large Data ◽  
Differential Regulation ◽  
Large Data Sets ◽  
Data Sets ◽  
Human Metabolism ◽  
Experimental Conditions ◽  
Systemic Model

Abstract Background Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism’s cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. Results We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. Conclusions Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

A device enabling single unit recordings during head movements in cats

1982 ◽  
Vol 8 (4) ◽  
pp. 443-444 ◽  
Author(s):  
J.S. Schneider ◽  
A.A. Castaldi ◽  
T.I. Lidsky
Keyword(s):  
Single Unit ◽  
Head Movements ◽  
Single Unit Recordings

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

2021 ◽  
Author(s):  
Xinxu Shen ◽  
Troy Houser ◽  
David Victor Smith ◽  
Vishnu P. Murty
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Individual Difference ◽  
Language Processing ◽  
Large Scale ◽  
High Reliability ◽  
Difference Analysis ◽  
Universal Sentence ◽  
Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

Single-unit recordings of arterial chemoreceptors from mouse petrosal ganglia in vitro

2000 ◽  
Vol 88 (4) ◽  
pp. 1489-1495 ◽  
Author(s):  
David F. Donnelly ◽  
Ricardo Rigual
Keyword(s):  
Mouse Model ◽  
Carotid Body ◽  
Unit Activity ◽  
Action Potentials ◽  
Single Unit ◽  
Gene Deletions ◽  
Suction Electrode ◽  
Single Unit Recordings ◽  
Unit Action

A preparation was developed that allows for the recording of single-unit chemoreceptor activity from mouse carotid body in vitro. An anesthetized mouse was decapitated, and each carotid body was harvested, along with the sinus nerve, glossopharyngeal nerve, and petrosal ganglia. After exposure to collagenase/trypsin, the cleaned complex was transferred to a recording chamber where it was superfused with oxygenated saline. The ganglia was searched for evoked or spontaneous unit activity by using a glass suction electrode. Single-unit action potentials were 57 ± 10 (SE) ( n = 16) standard deviations above the recording noise, and spontaneous spikes were generated as a random process. Decreasing superfusate[Formula: see text] to near 20 Torr caused an increase in spiking activity from 1.3 ± 0.4 to 14.1 ± 1.9 Hz ( n = 16). The use of mice for chemoreceptor studies may be advantageous because targeted gene deletions are well developed in the mouse model and may be useful in addressing unresolved questions regarding the mechanism of chemotransduction.

scholarly journals Trajectory Planning in UAV Emergency Networks with Potential Underlaying D2D Communication Based on K-means

2021 ◽  
Author(s):  
Shuo Zhang ◽  
Shuo Shi ◽  
Tianming Feng ◽  
Xuemai Gu
Keyword(s):  
Trajectory Planning ◽  
Communication Systems ◽  
High Speed ◽  
Large Scale ◽  
High Reliability ◽  
Base Station ◽  
Planning Problem ◽  
Communication Signal ◽  
User Coverage ◽  
Trajectory Length

Abstract Unmanned aerial vehicles (UAVs) have been widely used in communication systems due to excellent maneuverability and mobility. The ultra-high speed, ultra-low latency, and ultra-high reliability of 5th generation wireless systems (5G) have further promoted vigorous development of UAVs. Compared with traditional means of communication, UAV can provide services for ground terminal without time and space constraints, so it is often used as air base station (BS). Especially in emergency communications and rescue, it provides temporary communication signal coverage service for disaster areas. In the face of large-scale and scattered user coverage tasks, UAV's trajectory is an important factor affecting its energy consumption and communication performance. In this paper, we consider a UAV emergency communication network where UAV aims to achieve complete coverage of potential underlying D2D users (DUs). The trajectory planning problem is transformed into the deployment and connection problem of stop points (SPs). Aiming at trajectory length and sum throughput, two trajectory planning algorithms based on K-means are proposed. Due to the non-convexity of sum throughput optimization, we present a sub-optimal solution by using the successive convex approximation (SCA) method. In order to balance the relationship between trajectory length and sum throughput, we propose a joint evaluation index which is used as an objective function to further optimize trajectory. Simulation results show the validity of the proposed algorithms which have advantages over the well-known benchmark scheme in terms of trajectory length and sum throughput.

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