Publications by authors named "Stephen J Eglen"

45 Publications

Causality indices for bivariate time series data: A comparative review of performance.

Chaos 2021 Aug;31(8):083111

Cambridge Centre for Artificial Intelligence in Medicine and Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, United Kingdom.

Inferring nonlinear and asymmetric causal relationships between multivariate longitudinal data is a challenging task with wide-ranging application areas including clinical medicine, mathematical biology, economics, and environmental research. A number of methods for inferring causal relationships within complex dynamic and stochastic systems have been proposed, but there is not a unified consistent definition of causality in the context of time series data. We evaluate the performance of ten prominent causality indices for bivariate time series across four simulated model systems that have different coupling schemes and characteristics. Pairwise correlations between different methods, averaged across all simulations, show that there is generally strong agreement between methods, with minimum, median, and maximum Pearson correlations between any pair (excluding two similarity indices) of 0.298, 0.719, and 0.955, respectively. In further experiments, we show that these methods are not always invariant to real-world relevant transformations (data availability, standardization and scaling, rounding errors, missing data, and noisy data). We recommend transfer entropy and nonlinear Granger causality as particularly strong approaches for estimating bivariate causal relationships in real-world applications. Both successfully identify causal relationships and a lack thereof across multiple simulations, while remaining robust to rounding errors, at least 20% missing data and small variance Gaussian noise. Finally, we provide flexible open-access Python code for computation of these methods and for the model simulations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1063/5.0053519DOI Listing
August 2021

CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility.

F1000Res 2021 30;10:253. Epub 2021 Mar 30.

Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

The traditional scientific paper falls short of effectively communicating computational research.  To help improve this situation, we propose a system by which the computational workflows underlying research articles are checked. The CODECHECK system uses open infrastructure and tools and can be integrated into review and publication processes in multiple ways. We describe these integrations along multiple dimensions (importance, who, openness, when). In collaboration with academic publishers and conferences, we demonstrate CODECHECK with 25 reproductions of diverse scientific publications. These CODECHECKs show that asking for reproducible workflows during a collaborative review can effectively improve executability. While CODECHECK has clear limitations, it may represent a building block in Open Science and publishing ecosystems for improving the reproducibility, appreciation, and, potentially, the quality of non-textual research artefacts. The CODECHECK website can be accessed here: https://codecheck.org.uk/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.12688/f1000research.51738.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8311796PMC
September 2021

DeepClean: Self-Supervised Artefact Rejection for Intensive Care Waveform Data Using Deep Generative Learning.

Acta Neurochir Suppl 2021 ;131:235-241

Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK.

Waveform physiological data are important in the treatment of critically ill patients in the intensive care unit. Such recordings are susceptible to artefacts, which must be removed before the data can be reused for alerting or reprocessed for other clinical or research purposes. Accurate removal of artefacts reduces bias and uncertainty in clinical assessment, as well as the false positive rate of ICU alarms, and is therefore a key component in providing optimal clinical care. In this work, we present DeepClean, a prototype self-supervised artefact detection system using a convolutional variational autoencoder deep neural network that avoids costly and painstaking manual annotation, requiring only easily obtained 'good' data for training. For a test case with invasive arterial blood pressure, we demonstrate that our algorithm can detect the presence of an artefact within a 10s sample of data with sensitivity and specificity around 90%. Furthermore, DeepClean was able to identify regions of artefacts within such samples with high accuracy, and we show that it significantly outperforms a baseline principal component analysis approach in both signal reconstruction and artefact detection. DeepClean learns a generative model and therefore may also be used for imputation of missing data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-3-030-59436-7_45DOI Listing
June 2021

Ten simple rules for writing Dockerfiles for reproducible data science.

PLoS Comput Biol 2020 11 10;16(11):e1008316. Epub 2020 Nov 10.

School of Psychological Science, University of Bristol, Bristol, Great Britain.

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow's reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container's image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1008316DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654784PMC
November 2020

From random to regular: Variation in the patterning of retinal mosaics.

J Comp Neurol 2020 09 3;528(13):2135-2160. Epub 2020 Mar 3.

Neuroscience Research Institute, University of California at Santa Barbara, Santa Barbara, California.

The various types of retinal neurons are each positioned at their respective depths within the retina where they are believed to be assembled as orderly mosaics, in which like-type neurons minimize proximity to one another. Two common statistical analyses for assessing the spatial properties of retinal mosaics include the nearest neighbor analysis, from which an index of their "regularity" is commonly calculated, and the density recovery profile derived from autocorrelation analysis, revealing the presence of an exclusion zone indicative of anti-clustering. While each of the spatial statistics derived from these analyses, the regularity index and the effective radius, can be useful in characterizing such properties of orderly retinal mosaics, they are rarely sufficient for conveying the natural variation in the self-spacing behavior of different types of retinal neurons and the extent to which that behavior generates uniform intercellular spacing across the mosaic. We consider the strengths and limitations of these and other spatial statistical analyses for assessing the patterning in retinal mosaics, highlighting a number of misconceptions and their frequent misuse. Rather than being diagnostic criteria for determining simply whether a population is "regular," they should be treated as descriptive statistics that convey variation in the factors that influence neuronal positioning. We subsequently apply multiple spatial statistics to the analysis of eight different mosaics in the mouse retina, demonstrating conspicuous variability in the degree of patterning present, from essentially random to notably regular. This variability in patterning has both a developmental as well as a functional significance, reflecting the rules governing the positioning of different types of neurons as the architecture of the retina is assembled, and the distinct mechanisms by which they regulate dendritic growth to generate their characteristic coverage and connectivity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cne.24880DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368823PMC
September 2020

Functional characterization of human pluripotent stem cell-derived cortical networks differentiated on laminin-521 substrate: comparison to rat cortical cultures.

Sci Rep 2019 11 20;9(1):17125. Epub 2019 Nov 20.

Faculty of Medicine and Health Technology and BioMediTech, Tampere University, Tampere, Finland.

Human pluripotent stem cell (hPSC)-derived neurons provide exciting opportunities for in vitro modeling of neurological diseases and for advancing drug development and neurotoxicological studies. However, generating electrophysiologically mature neuronal networks from hPSCs has been challenging. Here, we report the differentiation of functionally active hPSC-derived cortical networks on defined laminin-521 substrate. We apply microelectrode array (MEA) measurements to assess network events and compare the activity development of hPSC-derived networks to that of widely used rat embryonic cortical cultures. In both of these networks, activity developed through a similar sequence of stages and time frames; however, the hPSC-derived networks showed unique patterns of bursting activity. The hPSC-derived networks developed synchronous activity, which involved glutamatergic and GABAergic inputs, recapitulating the classical cortical activity also observed in rodent counterparts. Principal component analysis (PCA) based on spike rates, network synchronization and burst features revealed the segregation of hPSC-derived and rat network recordings into different clusters, reflecting the species-specific and maturation state differences between the two networks. Overall, hPSC-derived neural cultures produced with a defined protocol generate cortical type network activity, which validates their applicability as a human-specific model for pharmacological studies and modeling network dysfunctions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-019-53647-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6868015PMC
November 2019

Burst Detection Methods.

Adv Neurobiol 2019 ;22:185-206

Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

'Bursting', defined as periods of high-frequency firing of a neuron separated by periods of quiescence, has been observed in various neuronal systems, both in vitro and in vivo. It has been associated with a range of neuronal processes, including efficient information transfer and the formation of functional networks during development, and has been shown to be sensitive to genetic and pharmacological manipulations. Accurate detection of periods of bursting activity is thus an important aspect of characterising both spontaneous and evoked neuronal network activity. A wide variety of computational methods have been developed to detect periods of bursting in spike trains recorded from neuronal networks. In this chapter, we review several of the most popular and successful of these methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-3-030-11135-9_8DOI Listing
August 2019

Recent developments in scholarly publishing to improve research practices in the life sciences.

Emerg Top Life Sci 2018 Dec;2(6):775-778

University of Cambridge, Cambridge, U.K.

We outline recent developments in scholarly publishing that we think will improve the working environment and career prospects for life scientists. Most prominently, we discuss two key developments. (1) Life scientists are now embracing a preprint culture leading to rapid dissemination of research findings. (2) We outline steps to overcome the reproducibility crisis. We also briefly describe other innovations in scholarly publishing, along with changes to open access mandates from funding agencies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1042/ETLS20180172DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289060PMC
December 2018

meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays.

PLoS Comput Biol 2018 10 1;14(10):e1006506. Epub 2018 Oct 1.

Cambridge Computational Biology Institute, University of Cambridge, Cambridge, United Kingdom.

Here we present an open-source R package 'meaRtools' that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL> = 3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1006506DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6181426PMC
October 2018

A molecular mechanism for the topographic alignment of convergent neural maps.

Elife 2017 03 14;6. Epub 2017 Mar 14.

CNRS UPR3212 - Institute of Cellular and Integrative Neuroscience, University of Strasbourg, Strasbourg, France.

Sensory processing requires proper alignment of neural maps throughout the brain. In the superficial layers of the superior colliculus of the midbrain, converging projections from retinal ganglion cells and neurons in visual cortex must be aligned to form a visuotopic map, but the basic mechanisms mediating this alignment remain elusive. In a new mouse model, ectopic expression of ephrin-A3 () in a subset of retinal ganglion cells, quantitatively altering the retinal EFNAs gradient, disrupts cortico-collicular map alignment onto the retino-collicular map, creating a visuotopic mismatch. Genetic inactivation of ectopic EFNA3 restores a wild-type cortico-collicular map. Theoretical analyses using a new mapping algorithm model both map formation and alignment, and recapitulate our experimental observations. The algorithm is based on an initial sensory map, the retino-collicular map, which carries intrinsic topographic information, the retinal EFNAs, to the superior colliculus. These EFNAs subsequently topographically align ingrowing visual cortical axons to the retino-collicular map.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.7554/eLife.20470DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5360444PMC
March 2017

Ten Simple Rules for Taking Advantage of Git and GitHub.

PLoS Comput Biol 2016 07 14;12(7):e1004947. Epub 2016 Jul 14.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1004947DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945047PMC
July 2016

A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks.

J Neurophysiol 2016 08 20;116(2):306-21. Epub 2016 Apr 20.

Cambridge Computational Biology Institute, University of Cambridge, Cambridge, United Kingdom; and.

Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide "perfect" burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1152/jn.00093.2016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4969396PMC
August 2016

Homeostatic Activity-Dependent Tuning of Recurrent Networks for Robust Propagation of Activity.

J Neurosci 2016 Mar;36(13):3722-34

Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, United Kingdom, Cambridge Computational Biology Institute, University of Cambridge, Cambridge CB3 0WA, United Kingdom.

Unlabelled: Developing neuronal networks display spontaneous bursts of action potentials that are necessary for circuit organization and tuning. While spontaneous activity has been shown to instruct map formation in sensory circuits, it is unknown whether it plays a role in the organization of motor networks that produce rhythmic output. Using computational modeling, we investigate how recurrent networks of excitatory and inhibitory neuronal populations assemble to produce robust patterns of unidirectional and precisely timed propagating activity during organism locomotion. One example is provided by the motor network inDrosophilalarvae, which generates propagating peristaltic waves of muscle contractions during crawling. We examine two activity-dependent models, which tune weak network connectivity based on spontaneous activity patterns: a Hebbian model, where coincident activity in neighboring populations strengthens connections between them; and a homeostatic model, where connections are homeostatically regulated to maintain a constant level of excitatory activity based on spontaneous input. The homeostatic model successfully tunes network connectivity to generate robust activity patterns with appropriate timing relationships between neighboring populations. These timing relationships can be modulated by the properties of spontaneous activity, suggesting its instructive role for generating functional variability in network output. In contrast, the Hebbian model fails to produce the tight timing relationships between neighboring populations required for unidirectional activity propagation, even when additional assumptions are imposed to constrain synaptic growth. These results argue that homeostatic mechanisms are more likely than Hebbian mechanisms to tune weak connectivity based on spontaneous input in a recurrent network for rhythm generation and robust activity propagation.

Significance Statement: How are neural circuits organized and tuned to maintain stable function and produce robust output? This task is especially difficult during development, when circuit properties change in response to variable environments and internal states. Many developing circuits exhibit spontaneous activity, but its role in the synaptic organization of motor networks that produce rhythmic output is unknown. We studied a model motor network, that when appropriately tuned, generates propagating activity as during crawling inDrosophilalarvae. Based on experimental evidence of activity-dependent tuning of connectivity, we examined plausible mechanisms by which appropriate connectivity emerges. Our results suggest that activity-dependent homeostatic mechanisms are better suited than Hebbian mechanisms for organizing motor network connectivity, and highlight an important difference from sensory areas.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.2511-15.2016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4812132PMC
March 2016

Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates.

J Biomol Screen 2016 Jun 29;21(5):510-9. Epub 2016 Mar 29.

Office of Research and Development, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA

We examined neural network ontogeny using microelectrode array (MEA) recordings made in multiwell MEA (mwMEA) plates over the first 12 days in vitro (DIV). In primary cortical cultures, action potential spiking activity developed rapidly between DIV 5 and 12. Spiking was sporadic and unorganized at early DIV, and became progressively more organized with time, with bursting parameters, synchrony, and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity; principal components analysis using these features demonstrated segregation of data by age at both the well and plate levels. Using random forest classifiers and support vector machines, we demonstrated that four features (coefficient of variation [CV] of within-burst interspike interval, CV of interburst interval, network spike rate, and burst rate) could predict the age of each well recording with >65% accuracy. When restricting the classification to a binary decision, accuracy improved to as high as 95%. Further, we present a novel resampling approach to determine the number of wells needed for comparing different treatments. Overall, these results demonstrate that network development on mwMEA plates is similar to development in single-well MEAs. The increased throughput of mwMEAs will facilitate screening drugs, chemicals, or disease states for effects on neurodevelopment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/1087057116640520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4904353PMC
June 2016

Geniculo-Cortical Projection Diversity Revealed within the Mouse Visual Thalamus.

PLoS One 2016 4;11(1):e0144846. Epub 2016 Jan 4.

MRC Centre for Developmental Neurobiology, King's College London, Guy's Campus, London, United Kingdom.

The mouse dorsal lateral geniculate nucleus (dLGN) is an intermediary between retina and primary visual cortex (V1). Recent investigations are beginning to reveal regional complexity in mouse dLGN. Using local injections of retrograde tracers into V1 of adult and neonatal mice, we examined the developing organisation of geniculate projection columns: the population of dLGN-V1 projection neurons that converge in cortex. Serial sectioning of the dLGN enabled the distribution of labelled projection neurons to be reconstructed and collated within a common standardised space. This enabled us to determine: the organisation of cells within the dLGN-V1 projection columns; their internal organisation (topology); and their order relative to V1 (topography). Here, we report parameters of projection columns that are highly variable in young animals and refined in the adult, exhibiting profiles consistent with shell and core zones of the dLGN. Additionally, such profiles are disrupted in adult animals with reduced correlated spontaneous activity during development. Assessing the variability between groups with partial least squares regression suggests that 4-6 cryptic lamina may exist along the length of the projection column. Our findings further spotlight the diversity of the mouse dLGN--an increasingly important model system for understanding the pre-cortical organisation and processing of visual information. Furthermore, our approach of using standardised spaces and pooling information across many animals will enhance future functional studies of the dLGN.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144846PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4699701PMC
July 2016

Editorial: Quantitative Analysis of Neuroanatomy.

Front Neuroanat 2015 11;9:143. Epub 2015 Nov 11.

Department of Systems Neuroscience, Medical Faculty, Ruhr University Bochum Bochum, Germany.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fnana.2015.00143DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4641246PMC
November 2015

Estimating the location and size of retinal injections from orthogonal images of an intact retina.

BMC Neurosci 2015 Nov 21;16:80. Epub 2015 Nov 21.

Cambridge Computational Biology Institute, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, UK.

Background: To study the mapping from the retina to the brain, typically a small region of the retina is injected with a dye, which then propagates to the retina's target structures. To determine the location of the injection, usually the retina is dissected out of the eye, flattened and then imaged, causing tears and stretching of the retina. The location of the injection is then estimated from the image of the flattened retina. Here we propose a new method that avoids dissection of the retina.

Results: We have developed IntactEye, a software package that uses two orthogonal images of the intact retina to locate focal injections of a dye. The two images are taken while the retina is still inside the eye. This bypasses the dissection step, avoiding unnecessary damage to the retina, and speeds up data acquisition. By using the native spherical coordinates of the eye, we avoid distortions caused by interpreting a curved structure in a flat coordinate system. Our method compares well to the projection method and to the Retistruct package, which both use the flattened retina as a starting point. We have tested the method also on synthetic data, where the injection location is known. Our method has been designed for analysing mouse retinas, where there are no visible landmarks for discerning retinal orientation, but can also be applied to retinas from other species.

Conclusions: IntactEye allows the user to precisely specify the location and size of a retinal injection from two orthogonal images taken of the eye. We are solving the abstract problem of locating a point on a spherical object from two orthogonal images, which might have applications outside the field of neuroscience.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12868-015-0217-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4654850PMC
November 2015

Canalization of genetic and pharmacological perturbations in developing primary neuronal activity patterns.

Neuropharmacology 2016 Jan 26;100:47-55. Epub 2015 Jul 26.

Genes to Cognition Programme, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK. Electronic address:

The function of the nervous system depends on the integrity of synapses and the patterning of electrical activity in brain circuits. The rapid advances in genome sequencing reveal a large number of mutations disrupting synaptic proteins, which potentially result in diseases known as synaptopathies. However, it is also evident that every normal individual carries hundreds of potentially damaging mutations. Although genetic studies in several organisms show that mutations can be masked during development by a process known as canalization, it is unknown if this occurs in the development of the electrical activity in the brain. Using longitudinal recordings of primary cultured neurons on multi-electrode arrays from mice carrying knockout mutations we report evidence of canalization in development of spontaneous activity patterns. Phenotypes in the activity patterns in young cultures from mice lacking the Gria1 subunit of the AMPA receptor were ameliorated as cultures matured. Similarly, the effects of chronic pharmacological NMDA receptor blockade diminished as cultures matured. Moreover, disturbances in activity patterns by simultaneous disruption of Gria1 and NMDA receptors were also canalized by three weeks in culture. Additional mutations and genetic variations also appeared to be canalized to varying degrees. These findings indicate that neuronal network canalization is a form of nervous system plasticity that provides resilience to developmental disruption. This article is part of the Special Issue entitled 'Synaptopathy--from Biology to Therapy'.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuropharm.2015.07.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726661PMC
January 2016

Quantitative differences in developmental profiles of spontaneous activity in cortical and hippocampal cultures.

Neural Dev 2015 Jan 28;10. Epub 2015 Jan 28.

Cambridge Computational Biology Institute, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, UK.

Background: Neural circuits can spontaneously generate complex spatiotemporal firing patterns during development. This spontaneous activity is thought to help guide development of the nervous system. In this study, we had two aims. First, to characterise the changes in spontaneous activity in cultures of developing networks of either hippocampal or cortical neurons dissociated from mouse. Second, to assess whether there are any functional differences in the patterns of activity in hippocampal and cortical networks.

Results: We used multielectrode arrays to record the development of spontaneous activity in cultured networks of either hippocampal or cortical neurons every 2 or 3 days for the first month after plating. Within a few days of culturing, networks exhibited spontaneous activity. This activity strengthened and then stabilised typically around 21 days in vitro. We quantified the activity patterns in hippocampal and cortical networks using 11 features. Three out of 11 features showed striking differences in activity between hippocampal and cortical networks: (1) interburst intervals are less variable in spike trains from hippocampal cultures; (2) hippocampal networks have higher correlations and (3) hippocampal networks generate more robust theta-bursting patterns. Machine-learning techniques confirmed that these differences in patterning are sufficient to classify recordings reliably at any given age as either hippocampal or cortical networks.

Conclusions: Although cultured networks of hippocampal and cortical networks both generate spontaneous activity that changes over time, at any given time we can reliably detect differences in the activity patterns. We anticipate that this quantitative framework could have applications in many areas, including neurotoxicity testing and for characterising the phenotype of different mutant mice. All code and data relating to this report are freely available for others to use.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13064-014-0028-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4320829PMC
January 2015

Quantitative assessment of computational models for retinotopic map formation.

Dev Neurobiol 2015 Jun 14;75(6):641-66. Epub 2014 Nov 14.

Cambridge Computational Biology Institute, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB3 0WA, United Kingdom.

Molecular and activity-based cues acting together are thought to guide retinal axons to their terminal sites in vertebrate optic tectum or superior colliculus (SC) to form an ordered map of connections. The details of mechanisms involved, and the degree to which they might interact, are still not well understood. We have developed a framework within which existing computational models can be assessed in an unbiased and quantitative manner against a set of experimental data curated from the mouse retinocollicular system. Our framework facilitates comparison between models, testing new models against known phenotypes and simulating new phenotypes in existing models. We have used this framework to assess four representative models that combine Eph/ephrin gradients and/or activity-based mechanisms and competition. Two of the models were updated from their original form to fit into our framework. The models were tested against five different phenotypes: wild type, Isl2-EphA3(ki/ki), Isl2-EphA3(ki/+), ephrin-A2,A3,A5 triple knock-out (TKO), and Math5(-/-) (Atoh7). Two models successfully reproduced the extent of the Math5(-/-) anteromedial projection, but only one of those could account for the collapse point in Isl2-EphA3(ki/+). The models needed a weak anteroposterior gradient in the SC to reproduce the residual order in the ephrin-A2,A3,A5 TKO phenotype, suggesting either an incomplete knock-out or the presence of another guidance molecule. Our article demonstrates the importance of testing retinotopic models against as full a range of phenotypes as possible, and we have made available MATLAB software, we wrote to facilitate this process.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/dneu.22241DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4497816PMC
June 2015

Detecting pairwise correlations in spike trains: an objective comparison of methods and application to the study of retinal waves.

J Neurosci 2014 Oct;34(43):14288-303

Cambridge Computational Biology Institute Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, United Kingdom.

Correlations in neuronal spike times are thought to be key to processing in many neural systems. Many measures have been proposed to summarize these correlations and of these the correlation index is widely used and is the standard in studies of spontaneous retinal activity. We show that this measure has two undesirable properties: it is unbounded above and confounded by firing rate. We list properties needed for a measure to fairly quantify and compare correlations and we propose a novel measure of correlation-the spike time tiling coefficient. This coefficient, the correlation index, and 33 other measures of correlation of spike times are blindly tested for the required properties on synthetic and experimental data. Based on this, we propose a measure (the spike time tiling coefficient) to replace the correlation index. To demonstrate the benefits of this measure, we reanalyze data from seven key studies, which previously used the correlation index to investigate the nature of spontaneous activity. We reanalyze data from β2(KO) and β2(TG) mutants, mutants lacking connexin isoforms, and also the age-dependent changes in wild-type and β2(KO) correlations. Reanalysis of the data using the proposed measure can significantly change the conclusions. It leads to better quantification of correlations and therefore better inference from the data. We hope that the proposed measure will have wide applications, and will help clarify the role of activity in retinotopic map formation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1523/JNEUROSCI.2767-14.2014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4205553PMC
October 2014

Can retinal ganglion cell dipoles seed iso-orientation domains in the visual cortex?

PLoS One 2014 24;9(1):e86139. Epub 2014 Jan 24.

Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany ; Institute for Nonlinear Dynamics, University of Göttingen, Göttingen, Germany ; Bernstein Center for Computational Neuroscience, Göttingen, Germany ; Bernstein Focus for Neurotechnology, Göttingen, Germany ; Center for Studies in Physics and Biology, The Rockefeller University, New York, New York, United States of America.

It has been argued that the emergence of roughly periodic orientation preference maps (OPMs) in the primary visual cortex (V1) of carnivores and primates can be explained by a so-called statistical connectivity model. This model assumes that input to V1 neurons is dominated by feed-forward projections originating from a small set of retinal ganglion cells (RGCs). The typical spacing between adjacent cortical orientation columns preferring the same orientation then arises via Moiré-Interference between hexagonal ON/OFF RGC mosaics. While this Moiré-Interference critically depends on long-range hexagonal order within the RGC mosaics, a recent statistical analysis of RGC receptive field positions found no evidence for such long-range positional order. Hexagonal order may be only one of several ways to obtain spatially repetitive OPMs in the statistical connectivity model. Here, we investigate a more general requirement on the spatial structure of RGC mosaics that can seed the emergence of spatially repetitive cortical OPMs, namely that angular correlations between so-called RGC dipoles exhibit a spatial structure similar to that of OPM autocorrelation functions. Both in cat beta cell mosaics as well as primate parasol receptive field mosaics we find that RGC dipole angles are spatially uncorrelated. To help assess the level of these correlations, we introduce a novel point process that generates mosaics with realistic nearest neighbor statistics and a tunable degree of spatial correlations of dipole angles. Using this process, we show that given the size of available data sets, the presence of even weak angular correlations in the data is very unlikely. We conclude that the layout of ON/OFF ganglion cell mosaics lacks the spatial structure necessary to seed iso-orientation domains in the primary visual cortex.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086139PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901677PMC
October 2014

Following the ontogeny of retinal waves: pan-retinal recordings of population dynamics in the neonatal mouse.

J Physiol 2014 Apr 23;592(7):1545-63. Epub 2013 Dec 23.

Institute of Neuroscience, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, UK.

The immature retina generates spontaneous waves of spiking activity that sweep across the ganglion cell layer during a limited period of development before the onset of visual experience. The spatiotemporal patterns encoded in the waves are believed to be instructive for the wiring of functional connections throughout the visual system. However, the ontogeny of retinal waves is still poorly documented as a result of the relatively low resolution of conventional recording techniques. Here, we characterize the spatiotemporal features of mouse retinal waves from birth until eye opening in unprecedented detail using a large-scale, dense, 4096-channel multielectrode array that allowed us to record from the entire neonatal retina at near cellular resolution. We found that early cholinergic waves propagate with random trajectories over large areas with low ganglion cell recruitment. They become slower, smaller and denser when GABAA signalling matures, as occurs beyond postnatal day (P) 7. Glutamatergic influences dominate from P10, coinciding with profound changes in activity dynamics. At this time, waves cease to be random and begin to show repetitive trajectories confined to a few localized hotspots. These hotspots gradually tile the retina with time, and disappear after eye opening. Our observations demonstrate that retinal waves undergo major spatiotemporal changes during ontogeny. Our results support the hypotheses that cholinergic waves guide the refinement of retinal targets and that glutamatergic waves may also support the wiring of retinal receptive fields.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1113/jphysiol.2013.262840DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3979611PMC
April 2014

Neural circuits for peristaltic wave propagation in crawling Drosophila larvae: analysis and modeling.

Front Comput Neurosci 2013 4;7:24. Epub 2013 Apr 4.

Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge Cambridge, UK.

Drosophila larvae crawl by peristaltic waves of muscle contractions, which propagate along the animal body and involve the simultaneous contraction of the left and right side of each segment. Coordinated propagation of contraction does not require sensory input, suggesting that movement is generated by a central pattern generator (CPG). We characterized crawling behavior of newly hatched Drosophila larvae by quantifying timing and duration of segmental boundary contractions. We developed a CPG network model that recapitulates these patterns based on segmentally repeated units of excitatory and inhibitory (EI) neuronal populations coupled with immediate neighboring segments. A single network with symmetric coupling between neighboring segments succeeded in generating both forward and backward propagation of activity. The CPG network was robust to changes in amplitude and variability of connectivity strength. Introducing sensory feedback via "stretch-sensitive" neurons improved wave propagation properties such as speed of propagation and segmental contraction duration as observed experimentally. Sensory feedback also restored propagating activity patterns when an inappropriately tuned CPG network failed to generate waves. Finally, in a two-sided CPG model we demonstrated that two types of connectivity could synchronize the activity of two independent networks: connections from excitatory neurons on one side to excitatory contralateral neurons (E to E), and connections from inhibitory neurons on one side to excitatory contralateral neurons (I to E). To our knowledge, such I to E connectivity has not yet been found in any experimental system; however, it provides the most robust mechanism to synchronize activity between contralateral CPGs in our model. Our model provides a general framework for studying the conditions under which a single locally coupled network generates bilaterally synchronized and longitudinally propagating waves in either direction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fncom.2013.00024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3616270PMC
April 2013

Parasol cell mosaics are unlikely to drive the formation of structured orientation maps in primary visual cortex.

Vis Neurosci 2012 Nov 30;29(6):283-99. Epub 2012 Oct 30.

Department of Applied Mathematics and Theoretical Physics, Cambridge Computational Biology Institute, University of Cambridge, Cambridge, UK.

The receptive fields of on- and off-center parasol cell mosaics independently tile the retina to ensure efficient sampling of visual space. A recent theoretical model represented the on- and off-center mosaics by noisy hexagonal lattices of slightly different density. When the two lattices are overlaid, long-range Moiré interference patterns are generated. These Moiré interference patterns have been suggested to drive the formation of highly structured orientation maps in visual cortex. Here, we show that noisy hexagonal lattices do not capture the spatial statistics of parasol cell mosaics. An alternative model based upon local exclusion zones, termed as the pairwise interaction point process (PIPP) model, generates patterns that are statistically indistinguishable from parasol cell mosaics. A key difference between the PIPP model and the hexagonal lattice model is that the PIPP model does not generate Moiré interference patterns, and hence stimulated orientation maps do not show any hexagonal structure. Finally, we estimate the spatial extent of spatial correlations in parasol cell mosaics to be only 200-350 μm, far less than that required to generate Moiré interference. We conclude that parasol cell mosaics are too disordered to drive the formation of highly structured orientation maps in visual cortex.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1017/S0952523812000338DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3515662PMC
November 2012

Neuronal clustering and fasciculation phenotype in Dscam- and Bax-deficient mouse retinas.

J Comp Neurol 2012 May;520(7):1349-64

Neuroscience Research Institute, Department of Molecular, Cellular and Developmental Biology, University of California at Santa Barbara, Santa Barbara, California 93106, USA.

Individual types of retinal neurons are distributed to minimize proximity to neighboring cells. Many of these same cell types extend dendrites to provide coverage of the retinal surface. These two cardinal features of retinal mosaics are disrupted, for certain cell types, in mice deficient for the Down syndrome cell adhesion molecule, Dscam, exhibiting an aberrant clustering of somata and fasciculation of dendrites. The Dscam mutant mouse retina also exhibits excess numbers of these same cell types. The present study compared these two features in Dscam mutant retinas with the Bax knockout retina, in which excess numbers of two of these cell types, the melanopsin-positive retinal ganglion cells (MRGCs) and the dopaminergic amacrine cells (DACs), are also present. Whole retinas were immunolabeled for both populations, and every labeled soma was plotted. For the MRGCs, we found a gene dosage effect for Dscam, with the Dscam+/- retinas showing smaller increases in cell number, clustering, and fasciculation. Curiously, Bax-/- retinas, showing numbers of MRGCs intermediate to those found in the Dscam-/- and Dscam+/- retinas, also had clustering and fasciculation phenotypes that were intermediate to retinas with those genotypes. DACs, by comparison, showed changes in both the Dscam-/- and the Bax-/- retinas that did not correlate with their increases in DAC number. The fasciculation phenotype in the Dscam-/- retina was particularly prominent despite only modest clustering. These results demonstrate that the somal clustering and fasciculation observed in the Dscam mutant retina are not unique to Dscam deficiency and are manifested distinctively by different retinal cell types.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cne.23033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3684991PMC
May 2012

Modeling developmental patterns of spontaneous activity.

Curr Opin Neurobiol 2011 Oct 16;21(5):679-84. Epub 2011 Jun 16.

Cambridge Computational Biology Institute, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK.

Spontaneous activity is found in many regions of the developing nervous system; such activity is thought to be instructive for guiding developmental processes. In particular, the developing retina generates correlated patterns of activity known as retinal waves. We review the main theoretical models that have been developed to study the mechanisms for generation and propagation of retinal waves. Much of the progress in this field has been due to the close interaction between experimentalists and theorists in analyzing and modeling spontaneous activity. We conclude by describing spontaneous activity models in other systems and suggestions for future modeling work.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.conb.2011.05.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3184139PMC
October 2011

Burst-time-dependent plasticity robustly guides ON/OFF segregation in the lateral geniculate nucleus.

PLoS Comput Biol 2009 Dec 24;5(12):e1000618. Epub 2009 Dec 24.

Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.

Spontaneous retinal activity (known as "waves") remodels synaptic connectivity to the lateral geniculate nucleus (LGN) during development. Analysis of retinal waves recorded with multielectrode arrays in mouse suggested that a cue for the segregation of functionally distinct (ON and OFF) retinal ganglion cells (RGCs) in the LGN may be a desynchronization in their firing, where ON cells precede OFF cells by one second. Using the recorded retinal waves as input, with two different modeling approaches we explore timing-based plasticity rules for the evolution of synaptic weights to identify key features underlying ON/OFF segregation. First, we analytically derive a linear model for the evolution of ON and OFF weights, to understand how synaptic plasticity rules extract input firing properties to guide segregation. Second, we simulate postsynaptic activity with a nonlinear integrate-and-fire model to compare findings with the linear model. We find that spike-time-dependent plasticity, which modifies synaptic weights based on millisecond-long timing and order of pre- and postsynaptic spikes, fails to segregate ON and OFF retinal inputs in the absence of normalization. Implementing homeostatic mechanisms results in segregation, but only with carefully-tuned parameters. Furthermore, extending spike integration timescales to match the second-long input correlation timescales always leads to ON segregation because ON cells fire before OFF cells. We show that burst-time-dependent plasticity can robustly guide ON/OFF segregation in the LGN without normalization, by integrating pre- and postsynaptic bursts irrespective of their firing order and over second-long timescales. We predict that an LGN neuron will become ON- or OFF-responsive based on a local competition of the firing patterns of neighboring RGCs connecting to it. Finally, we demonstrate consistency with ON/OFF segregation in ferret, despite differences in the firing properties of retinal waves. Our model suggests that diverse input statistics of retinal waves can be robustly interpreted by a burst-based rule, which underlies retinogeniculate plasticity across different species.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1000618DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2790088PMC
December 2009

A multi-component model of the developing retinocollicular pathway incorporating axonal and synaptic growth.

PLoS Comput Biol 2009 Dec 11;5(12):e1000600. Epub 2009 Dec 11.

Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.

During development, neurons extend axons to different brain areas and produce stereotypical patterns of connections. The mechanisms underlying this process have been intensively studied in the visual system, where retinal neurons form retinotopic maps in the thalamus and superior colliculus. The mechanisms active in map formation include molecular guidance cues, trophic factor release, spontaneous neural activity, spike-timing dependent plasticity (STDP), synapse creation and retraction, and axon growth, branching and retraction. To investigate how these mechanisms interact, a multi-component model of the developing retinocollicular pathway was produced based on phenomenological approximations of each of these mechanisms. Core assumptions of the model were that the probabilities of axonal branching and synaptic growth are highest where the combined influences of chemoaffinity and trophic factor cues are highest, and that activity-dependent release of trophic factors acts to stabilize synapses. Based on these behaviors, model axons produced morphologically realistic growth patterns and projected to retinotopically correct locations in the colliculus. Findings of the model include that STDP, gradient detection by axonal growth cones and lateral connectivity among collicular neurons were not necessary for refinement, and that the instructive cues for axonal growth appear to be mediated first by molecular guidance and then by neural activity. Although complex, the model appears to be insensitive to variations in how the component developmental mechanisms are implemented. Activity, molecular guidance and the growth and retraction of axons and synapses are common features of neural development, and the findings of this study may have relevance beyond organization in the retinocollicular pathway.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1000600DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2782179PMC
December 2009
-->