Biology subjects

Stephen Eglen

Publications and source records attributed to Stephen Eglen.

4 recordsLinked to original sources

bioRxiv2016

Towards standard practices for sharing computer code and programs in neuroscience

Background Background References Many areas of neuroscience are now critically dependent on computational tools to help understand the large volumes of data being created. Furthermore, computer models are increasingly being used to help predict and understand the function of the nervous system. Many of these computations are complex and usually cannot be concisely reported in the methods section of a scientific article. In a few areas there are widely used software packages for analysis (e.g., SPM, FSL, AFNI, FreeSurfer, Civet in neuroimaging) or simulation (e.g. NEURON, NEST, Brian). However, we often write new computer programs to solve specific problems in the course of our research. Some of these programs may be relatively small scripts that help analyze all of our data, and these rarely get described in papers. As authors, how best can we maximize the chances that other scientists can reprod ...

Neuroscience↗

bioRxiv2015

Homeostatic activity-dependent tuning of recurrent networks for robust propagation of activity

Developing neuronal networks display spontaneous rhythmic 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 in Drosophila larvae, 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 tunes network connectivity to generate robust activity patterns with the 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 local activity patterns in a recurrent network for rhythm generation and propagation.

Neuroscience↗

bioRxiv2014

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

BackgroundNeural 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.\n\nResultsWe used multielectrode arrays to record the development of spontaneous activity in cultured networks of either hippocampal or cortical neurons every two or three 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 eleven features. Three out of eleven 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. 3: Hippocampal networks generate more robust theta bursting patterns. Machine learning techniques confirmed that these differences in patterning are sufficient to reliably classify recordings at any given age as either hippocampal or cortical networks.\n\nConclusionsAlthough 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 phenotype of different mutant mice. All code and data relating to this report are freely available for others to use.

Neuroscience↗

bioRxiv2013

A data repository and analysis framework for spontaneous neural activity recordings in developing retina

BackgroundDuring early development, neural circuits fire spontaneously, generating activity episodes with complex spatiotemporal patterns. Recordings of spontaneous activity have been made in many parts of the nervous system over the last 25 years, reporting developmental changes in activity patterns and the effects of various genetic perturbations.\n\nResultsWe present a curated repository of multielectrode array recordings of spontaneous activity in developing mouse and ferret retina. The data have been annotated with minimal metadata and converted into HDF5. This paper describes the structure of the data, along with examples of reproducible research using these data files. We also demonstrate how these data can be analysed in the CARMEN workflow system. This article is written as a literate programming document; all programs and data described here are freely available.\n\nConclusions1. We hope this repository will lead to novel analysis of spontaneous activity recorded in different laboratories. 2. We encourage published data to be added to the repository. 3. This repository serves as an example of how multielectrode array recordings can be stored for long-term reuse.

Neuroscience↗