Neural systems & circuits最新文献

{"title":"A new measure based on degree distribution that links information theory and network graph analysis.","authors":"Michael W Hadley,&nbsp;Matt F McGranaghan,&nbsp;Aaron Willey,&nbsp;Chun Wai Liew,&nbsp;Elaine R Reynolds","doi":"10.1186/2042-1001-2-7","DOIUrl":"https://doi.org/10.1186/2042-1001-2-7","url":null,"abstract":"<p><strong>Background: </strong>Detailed connection maps of human and nonhuman brains are being generated with new technologies, and graph metrics have been instrumental in understanding the general organizational features of these structures. Neural networks appear to have small world properties: they have clustered regions, while maintaining integrative features such as short average pathlengths.</p><p><strong>Results: </strong>We captured the structural characteristics of clustered networks with short average pathlengths through our own variable, System Difference (SD), which is computationally simple and calculable for larger graph systems. SD is a Jaccardian measure generated by averaging all of the differences in the connection patterns between any two nodes of a system. We calculated SD over large random samples of matrices and found that high SD matrices have a low average pathlength and a larger number of clustered structures. SD is a measure of degree distribution with high SD matrices maximizing entropic properties. Phi (Φ), an information theory metric that assesses a system's capacity to integrate information, correlated well with SD - with SD explaining over 90% of the variance in systems above 11 nodes (tested for 4 to 13 nodes). However, newer versions of Φ do not correlate well with the SD metric.</p><p><strong>Conclusions: </strong>The new network measure, SD, provides a link between high entropic structures and degree distributions as related to small world properties.</p>","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"2 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-2-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30713191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From A to Z: a potential role for grid cells in spatial navigation.","authors":"Caswell Barry,&nbsp;Daniel Bush","doi":"10.1186/2042-1001-2-6","DOIUrl":"https://doi.org/10.1186/2042-1001-2-6","url":null,"abstract":"<p><p> Since their discovery, the strikingly regular and spatially stable firing of entorhinal grid cells has attracted the attention of experimentalists and theoreticians alike. The bulk of this work has focused either on the assumption that the principal role of grid cells is to support path integration or the extent to which their multiple firing locations can drive the sparse activity of hippocampal place cells. Here, we propose that grid cells are best understood as part of a network that combines self-motion and environmental cues to accurately track an animal's location in space. Furthermore, that grid cells - more so than place cells - efficiently encode self-location in allocentric coordinates. Finally, that the regular structure of grid firing fields represents information about the relative structure of space and, as such, may be used to guide goal directed navigation.</p>","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"2 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2012-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-2-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30654300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel, jitter-based method for detecting and measuring spike synchrony and quantifying temporal firing precision.","authors":"Ariel Agmon","doi":"10.1186/2042-1001-2-5","DOIUrl":"https://doi.org/10.1186/2042-1001-2-5","url":null,"abstract":"<p><strong>Background: </strong>Precise spike synchrony, at the millisecond or even sub-millisecond time scale, has been reported in different brain areas, but its neurobiological meaning and its underlying mechanisms remain unknown or controversial. Studying these questions is complicated by the lack of a validated, well-normalized and robust index for quantifying synchrony. Previously used measures of synchrony are often improperly normalized and thereby are not comparable between different experimental conditions, are sensitive to variations in firing rate or to the firing rate differential between the two neurons, and/or rely on untenable assumptions of firing rate stationarity and Poisson statistics. I describe here a novel measure, the Jitter-Based Synchrony Index (JBSI), that overcomes these issues.</p><p><strong>Results and discussion: </strong>The JBSI method is based on the introduction of virtual spike jitter. While previous implementations of the jitter method used it only to detect synchrony, the JBSI method also quantifies synchrony. Previous implementations of the jitter method used computationally intensive Monte Carlo simulations to generate surrogate spike trains, whereas the JBSI is computed analytically. The JBSI method does not assume any specific firing model, and does not require that the spike trains be locked to a repeating external stimulus. The JBSI can assume values from 1 (maximal possible synchrony) to -1 (minimal possible synchrony) and is therefore properly normalized. Using simulated Poisson spike trains with introduced controlled spike coincidences, I demonstrate that the JBSI is a linear measure of the spike coincidence rate, is independent of the mean firing frequency or the firing frequency differential between the two neurons, and is not sensitive to co-modulations in the firing rates of the two neurons. In contrast, several commonly used synchrony indices fail under one or more of these scenarios. I also demonstrate how the JBSI can be used to estimate the spike timing precision in the system.</p><p><strong>Conclusions: </strong>The JBSI is a conceptually simple and computationally efficient method that can be used to compute the statistical significance of firing synchrony, to quantify synchrony as a well-normalized index, and to estimate the degree of temporal precision in the system.</p>","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"2 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2012-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-2-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30587238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed network organization underlying feeding behavior in the mollusk Lymnaea.","authors":"Paul R Benjamin","doi":"10.1186/2042-1001-2-4","DOIUrl":"https://doi.org/10.1186/2042-1001-2-4","url":null,"abstract":"<p><p> The aim of the work reviewed here is to relate the properties of individual neurons to network organization and behavior using the feeding system of the gastropod mollusk, Lymnaea. Food ingestion in this animal involves sequences of rhythmic biting movements that are initiated by the application of a chemical food stimulus to the lips and esophagus. We investigated how individual neurons contribute to various network functions that are required for the generation of feeding behavior such as rhythm generation, initiation ('decision making'), modulation and hunger and satiety. The data support the view that feeding behavior is generated by a distributed type of network organization with individual neurons often contributing to more than one network function, sharing roles with other neurons. Multitasking in a distributed type of network would be 'economically' sensible in the Lymnaea feeding system where only about 100 neurons are available to carry out a variety of complex tasks performed by millions of neurons in the vertebrate nervous system. Having complementary and potentially alternative mechanisms for network functions would also add robustness to what is a 'noisy' network where variable firing rates and synaptic strengths are commonly encountered in electrophysiological recording experiments.</p>","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"2 ","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2012-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-2-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30579658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 9th annual computational and systems neuroscience (cosyne) meeting.","authors":"Agnieszka Grabska-Barwińska,&nbsp;Cindy Poo","doi":"10.1186/2042-1001-2-3","DOIUrl":"https://doi.org/10.1186/2042-1001-2-3","url":null,"abstract":"<p><p> The 9th annual Computational and Systems Neuroscience meeting (Cosyne) was held 23-26 February in Salt Lake City, Utah. Cosyne meeting is the forum for exchange of experimental and theoretical/computational approaches to studying systems neuroscience.</p>","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"2 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2012-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-2-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30541844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From baconian to popperian neuroscience.","authors":"David Gamez","doi":"10.1186/2042-1001-2-2","DOIUrl":"https://doi.org/10.1186/2042-1001-2-2","url":null,"abstract":"<p><p>The development of neuroscience over the past 50 years has some similarities with the development of physics in the 17th century. Towards the beginning of that century, Bacon promoted the systematic gathering of experimental data and the induction of scientific truth; towards the end, Newton expressed his principles of gravitation and motion in a concise set of mathematical equations that made precise falsifiable predictions. This paper expresses the opinion that as neuroscience comes of age, it needs to move away from amassing large quantities of data about the brain, and adopt a popperian model in which theories are developed that can make strong falsifiable predictions and guide future experimental work. </p>","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"2 ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2012-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-2-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30457014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Looking back on the first year of Neural Systems & Circuits.","authors":"Peter E Latham,&nbsp;Venkatesh N Murthy","doi":"10.1186/2042-1001-2-1","DOIUrl":"https://doi.org/10.1186/2042-1001-2-1","url":null,"abstract":"","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"2 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2012-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-2-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30457508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coverage, continuity, and visual cortical architecture.","authors":"Wolfgang Keil, Fred Wolf","doi":"10.1186/2042-1001-1-17","DOIUrl":"10.1186/2042-1001-1-17","url":null,"abstract":"<p><strong>Background: </strong>The primary visual cortex of many mammals contains a continuous representation of visual space, with a roughly repetitive aperiodic map of orientation preferences superimposed. It was recently found that orientation preference maps (OPMs) obey statistical laws which are apparently invariant among species widely separated in eutherian evolution. Here, we examine whether one of the most prominent models for the optimization of cortical maps, the elastic net (EN) model, can reproduce this common design. The EN model generates representations which optimally trade of stimulus space coverage and map continuity. While this model has been used in numerous studies, no analytical results about the precise layout of the predicted OPMs have been obtained so far.</p><p><strong>Results: </strong>We present a mathematical approach to analytically calculate the cortical representations predicted by the EN model for the joint mapping of stimulus position and orientation. We find that in all the previously studied regimes, predicted OPM layouts are perfectly periodic. An unbiased search through the EN parameter space identifies a novel regime of aperiodic OPMs with pinwheel densities lower than found in experiments. In an extreme limit, aperiodic OPMs quantitatively resembling experimental observations emerge. Stabilization of these layouts results from strong nonlocal interactions rather than from a coverage-continuity-compromise.</p><p><strong>Conclusions: </strong>Our results demonstrate that optimization models for stimulus representations dominated by nonlocal suppressive interactions are in principle capable of correctly predicting the common OPM design. They question that visual cortical feature representations can be explained by a coverage-continuity-compromise.</p>","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"1 ","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2011-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3283456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30455665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Q&A: What is the Open Connectome Project?","authors":"Joshua T Vogelstein","doi":"10.1186/2042-1001-1-16","DOIUrl":"https://doi.org/10.1186/2042-1001-1-16","url":null,"abstract":"","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"1 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2011-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-1-16","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30455818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EMBO Conference Series on the Assembly and Function of Neuronal Circuits.","authors":"Alice Y Wang,&nbsp;Jeremiah Y Cohen","doi":"10.1186/2042-1001-1-15","DOIUrl":"https://doi.org/10.1186/2042-1001-1-15","url":null,"abstract":"<p><p> The 2011 EMBO Conference Series on the Assembly and Function of Neuronal Circuits was held from 25 to 30 September 2011 at Monte Verità in Ascona, Switzerland. Approximately 100 participants explored current challenges and approaches to studying neural circuit function and organization.</p>","PeriodicalId":89606,"journal":{"name":"Neural systems & circuits","volume":"1 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/2042-1001-1-15","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30456000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}