Open Science

Neuroscience is often said to be entering its age of "Big Data". Remarkable technical progress in the last decades means that we are now able to record the activity of hundreds to thousands of neurons in behaving animals at relevant timescales. This is an exciting prospect, but the richness and depth of these datasets presents a tremendous analytical challenge - one that I believe we will only surpass through global cooperation.

We live in an age where information technology allows us to manage and promote collaboration more efficiently and quickly than ever. Why should the greatest scientific contributions and ideas come only from people based at elite institutions located in wealthy countries? Assuming so and acting as if this were true entails an unacceptable waste of human talent and potential. 

It is just as crucial that Neuroscience becomes Open Source as it is that it acquires Big Data. We are at an age where as a community we can wield the power of information technology to make radical improvements in the ways we publish, assess and replicate scientific findings. Only by following this path will we achieve meaningful and lasting scientific - and societal - progress.

 
 

Recording from the same neuron in vivo using patch-clamp and Neuropixel probes.

Extracellular recordings have long been one of the workhorses of neuroscience. As our capability to record from increasing numbers of neurons grows, so does the challenge involved in spike-sorting and parsing these immense recordings into units. In this project at the Kampff Lab, done in the context of the Neuropixels Consortium, I have recorded the activity of the same cortical pyramidal neuron using Neuropixel probes and patch-clamp in anaesthetised rats. 

The dataset is available for download here and I've set up a companion GitHub repository here.

Please give the preprint a read, as it contains important information about the dataset.

We're using the Github repository to:

  1. Provide important information about the recordings;
  2. Share our analysis and figure generation code for transparency and replication purposes;
  3. Collaborate with interested scientist all over the world on follow-up projects based on this dataset.