João Pedro Gomes da Silva Frazão

Bonsai: an event-based framework for processing and controlling data streams

The design of modern scientific experiments requires the control and monitoring of many different data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsais core principles and architecture and demonstrate how it allows for the rapid and flexible prototyping of integrated experimental designs in neuroscience. We specifically highlight some applications that require the combination of many different hardware and software components, including video tracking of behavior, electrophysiology and closed-loop control of stimulation.

YVision: A General Purpose Software Composition Framework

Expectations for the industry of Human Computer Interaction are much higher today than they were ten or even five years ago. Innovative solutions to sense and gather information from the real world in real-time must be combined with lightning-fast computer graphics to deliver high-quality designs for the new interaction paradigms. The very combination of all these emerging technologies presents difficult challenges, not only for finding good design and programming methodologies, but to encapsulate those patterns in a collection of frameworks and tools enabling rapid-prototyping and agile development. Application designers should be able to express their creative endeavours by quickly trying out different design combinations with full access to leading edge technology. In the following we present the YVision general purpose software composition framework and show how it achieves the goal of managing the complexity and reducing the development time of parallel, data-driven, multimedia applications.

Recording from the same neuron with high-density CMOS probes and patch-clamp: a ground-truth dataset and an experiment in collaboration.

We built a rig to perform patch-clamp and extracellular recordings from the same neuron in vivo. In this setup, the axes of two micromanipulators are precisely aligned and their relative position tracked in real-time, allowing us to accurately target patch-clamp recordings to neurons near an extracellular probe. We used this setup to generate a publicly-available dataset where a cortical neuron’s spiking activity is recorded in patch-clamp next to a dense CMOS Neuropixels probe. “Ground-truth” datasets of this kind are rare but valuable to the neuroscience community, as they power the development and improvement of spike-sorting and analysis algorithms, tethering them to empirical observations. In this article, we describe our approach and report exploratory and descriptive analysis on the resulting dataset. We study the detectability of patch-clamp spikes on the extracellular probe, within-unit reliability of spike features and spatiotemporal dynamics of the action potential waveform. We open discussion and collaboration on this dataset through an online repository, with a view to producing follow-up publications. Prologue Our efforts to record from the same neuron in vivo using patch-clamp and dense extracellular probes have resulted in three outputs: a publicly-available dataset (http://bit.ly/paired_recs), a manuscript, and a code repository (http://bit.ly/paired_git). Together, these three components form the publication arising from the experiments we have performed. The role of the dataset is to be downloaded and re-used. The role of the manuscript is to describe the experimental methods through which we acquired the dataset, explain it and showcase which types of questions it can be used to address. The repository has two roles: first, promoting reproducibility and error correction. By making our analysis and figure-generation code freely-available, we wish to make our analysis procedures clear and enable the reader to reproduce our results from the raw data, alerting us to any potential mistakes. Second, the repository will form a living, dynamic and interactive component of the publication: a forum for open collaboration on this dataset. Any interested scientists can contribute to it, joining us in detailed exploration of these recordings with a view to producing follow-up publications in which they will be credited for their input. Why did we opt to publish this way? The first reason is that the very nature of the project we here describe – recording the same neuron with patch-clamp and extracellular probes – invites an open science and open source approach. This is because the primary use of this type of “ground truth” validation data is to aid the development of new sorting and analysis algorithms, as well as to benchmark and improve existing ones. The second reason is that despite being conceptually very simple, this project generated a large and complex dataset that can be tackled in many ways and used to address different types of question. Some of these questions are beyond the reach of our analytical expertise; others lie even beyond the scope of our scientific imagination. By releasing the dataset and providing a repository for scientific discussion and collaboration, we aim to maximise its scientific return to the community. Instead of having each interested research group work in isolation, we hope that by encouraging collaboration and discussion between peers we can foster synergy between them that will lead to work of greater scientific value. Although datasets like ours are exquisitely suited for such an approach, we believe this publication strategy needs to become more widely adopted in neuroscience. We were pleased to note recent publications spontaneously and independently using similar approaches1-4, in what may well be evidence of convergent thinking. Perhaps the time has come for new publication and collaboration paradigms. We will elaborate on this subject during the Epilogue. For now, let us get back to electrophysiological recordings, before we begin an experiment on scientific collaboration.

Why not record from every channel with a CMOS scanning probe

Neural recording devices normally require one output connection for each electrode. This constrains the number of electrodes that can be accommodated by the thin shafts of implantable probes. Sharing a single output connection between multiple electrodes relaxes this constraint and permits designs of ultra-high density neural probes. Here we report the design and in vivo validation of such a device, a complementary metal-oxide-semiconductor (CMOS) scanning probe with 1344 electrodes and 12 reference electrodes along an 8.1 mm × 100 μm × 50 μm shaft; the outcome of the European research project NeuroSeeker. This technology presented new challenges for data management and visualization, and we also report new methods addressing these challenges developed within NeuroSeeker. Scanning CMOS technology allows the fabrication of much smaller, denser electrode arrays. To help design electrode configurations for future probes, several recordings from many different brain regions were made with an ultra-dense passive probe fabricated using CMOS process. All datasets are available online.

Does Impedance Matter When Recording Spikes With Polytrodes

Extracellular microelectrodes have been widely used to measure brain activity, yet there are still basic questions about the requirements for a good extracellular microelectrode. One common source of confusion is how much an electrode’s impedance affects the amplitude of extracellular spikes and background noise. Here we quantify the effect of an electrode’s impedance on data quality in extracellular recordings, which is crucial for both the detection of spikes and their assignment to the correct neurons. This study employs commercial polytrodes containing 32 electrodes (177 μm2) arranged in a dense array. This allowed us to directly compare, side-by-side, the same extracellular signals measured by modified low impedance (∼100 kΩ) microelectrodes with unmodified high impedance (∼1 MΩ) microelectrodes. We begin with an evaluation of existing protocols to lower the impedance of the electrodes. The poly (3,4-ethylenedioxythiophene)-polystyrene sulfonate (PEDOT-PSS) electrodeposition protocol is a simple, stable, and reliable method for decreasing the impedance of a microelectrode up to 10-fold. We next record in vivo using polytrodes that are modified in a ‘chess board’ pattern, such that the signal of one neuron is detected by multiple coated and non-coated electrodes. The performance of the coated and non-coated electrodes is then compared on measures of background noise and amplitude of the detected action potentials. If the proper recording system is used, then the impedance of a microelectrode within the range of standard polytrodes (∼0.1 to 2 MΩ) does not greatly affect data quality and spike sorting. This study should encourage neuroscientists to stop worrying about one more unknown.