I submitted a review of a paper for publication in the Journal of Neuroscience‘s Journal Club series. The original paper is here (sadly paywalled) – it’s an interesting modelling study that attempts to fit simplified models of neuron population dynamics to experimental recordings to shed some light on the neural network dynamics underlying those recordings. Unfortunately J. Neurosci. made a terrible mistake and chose not to publish my submission, so instead of letting that work go to waste I’ve published it on The Winnower, an innovative open platform for publishing papers without pre-publication peer review. Instead, your article is public immediately, and readers can submit public post-publication reviews (and the article can be updated). This is clearly the future of scientific publishing, and some other bigger publishers (e.g. F1000) are already using similar models. The Winnower is a cool independent alternative, and is currently free to use. I really hope it takes off, but in the current impact-obsessed environment it’s fighting an uphill battle.
I was at the Organization for Computational Neuroscience annual meeting (CNS 2014) in Quebec City all last week, which I aim to blog about in the near future (if you’re keen you can see my posters on figshare), but before that, I should write about our paper. It’s been available online since the end of May (open access) but I’ve been tidying up bits and pieces of the code so haven’t got round to advertising it much.
The basic motivation behind the paper is the current lack of knowledge about the relationship between the voltage measurements made using extracellular electrodes (local field potentials – LFPs) and the activity of the neurons that underlies those measurements. It is very difficult to infer how currents are flowing in groups of neurons given a set of extracellular voltage measurements, as an infinite number of arrangements of current sources can give rise to the same LFP. Our approach instead was to take a particular pattern of activity in a neural network that was already well characterised experimentally, and to predict given this pattern what the extracellular voltage measurements would be given the physics of current flow in biological tissue. This is the “forward modelling” approach used previously in various studies (see here for a recent review and description of this approach). Our paper describes a simulation tool for performing these simulations (the Virtual Electrode Recording Tool for EXtracellular potentials: VERTEX), as well as some results from a large network model that we compared directly with experimental data.
The simulation tool is written in Matlab (sorry Python aficionados…) and can run in parallel if you have the Parallel Computing Toolbox installed. Matlab is often thought of as being slow, but if you’re cunning you can get things to run surprisingly speedily, which we have managed to do to a reasonable extent with VERTEX I think. You can download it from www.vertexsimulator.org – the download also includes files to run the model described in the paper, as well as some tutorials for setting up simulations. We’ve also made the experimental data available on figshare.
So now you know a little bit about what I was doing all those years up in the City of Dreams…
Tomsett RJ, Ainsworth M, Thiele A, Sanayei M et al. Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue. Brain Structure and Function (2014) doi:10.1007/s00429-014-0793-x