Weeks 3-4 Electrophysiology

The CMOS revolution Complementary-symmetry Metal Oxide Semiconductor (CMOS) circuits form the basis of modern electronics because they make it possible to perform logical operations without drawing any stable-state current. Such integrated circuitry has facilitated the development of extremely small and efficient technology, including microprocessors, static RAM and microcontrollers. For example, the technology that produced smartphones would arguably not have been possible without CMOS integrated circuitry. This technology is likewise transforming neuroscience, particularly in terms of equipment for electrophysiological data acquisition, which we will focus on in this week’s blog.   CMOS probes CMOS tech. has enabled the fabrication of high density multichannel silicon-based probes, because large numbers of recording sites can be sampled in series with only a few wires. These probes are perhaps the best available tools for obtaining action potential firing patterns of large groups of neurons in live animals. These probes consist of a ~10mm shank with up to thousands of recording sites distributed along it. Each recording site detects local voltage fluctuations relative to preselected reference sites. The large number of recording sites and the density of their arrangement along the probe makes it possible to acquire local voltage changes in the brain with excellent temporal precision and spatial resolution. This has enabled accurate spike sorting of a much larger number of neurons than was previously possible.   Experimenting with NeuroSeeker probe Our assignment this week was to plan and help execute an experiment using one such multichannel probe, the ‘NeuroSeeker’ probe (more info: http://www.neuroseeker.eu/). Joana Neto is currently characterising the probe as part of her PhD and she kindly allowed us to join her experiment this week. Experienced members of Adam Kampff’s group (Joana Neto, Joana Nogueira, George Dimitriadis and Lorenza Calcaterra) carried out surgery and handling of the probe. The NeuroSeeker probe has 1440 sites in total - 1336 recording sites and 96 ground reference sites. These sites are 20µm x 20µm and are arranged in 4 columns, evenly distributed (2.5µm spacing) along the shank of the probe.   Probing the auditory system - the plan In addition to learning how high-density probes work we had two experimental objectives:

• to acquire and analyse data obtained with a probe
• to address a scientific question of our choice within the auditory cortex

We wanted to learn something about auditory processing across cortical columns in the auditory cortex and planned to insert our probe into layer 5 of the auditory cortex parallel with the layer such that it would span a reasonable number of neurons with similar preferred frequencies (Figure 2). Figure 2) schematic depicting our planned experiment, with a Neuroseeker probe inserted roughly parallel with the deep layers of the auditory cortex. We designed auditory tests to investigate a couple of questions that we wanted to address:

• Is neuronal stimulus-specific adaptation (SSA) uniform across frequency-matched neurons in auditory layer 5 neurons?
• Is bandwidth encoded along the dorsal-ventral axis of the auditory cortex?
• Do all neurons respond the same way to harmonics?

  Probing the auditory system - the experiment We inserted the probe gradually into the region of interest and found a region with a lot of spontaneous activity. We were able to visualise this activity across all the channels simultaneously using a Bonsai plugin in real time along the probe during the experiment that is shown in the video below! <video> Unfortunately for a variety of reasons we did not find a population of neurons suitable for our auditory tasks, but we got a lot of spontaneous recordings that we could start to analyse. Hopefully we will get another chance to attempt this in the next couple of weeks (watch this space for updates).   Analysis Adam encouraged us to start by investigating the data from first principles (on a very limited timescale) to get a feel for the data and better understand the challenges that automated spike-sorting entails. We focused on the 480 channels that seemed to have the most activity and applied a simple threshold criterion to identify regions containing putative spikes (figure 3A). The minimum value in this range was labelled as the ‘event location’ and the subset of the data was visualized as a raster plot (figure 3B). We identified most of the events that were visible by eye, but obviously such a crude technique has a large number of false negatives and false positives. We nonetheless took the sum of all detected events in each channel as a first approximation. Surprisingly, even with this very crude method it is possible to see some structure in the data arising from different neurons. Later, Jorge Aurelius from the Gatsby Unit might try to use these data to find clusters, using expectation-maximisation, an algorithm that we discussed in the machine learning class. Figure 3) A) 10s of raw traces with events crossing the threshold (dotted line) marked with a black circle. B) Each event in the same time window is shown as a raster plot, and the total number of spikes detected in each channel over the entire recording is plotted as a heat map in C.   Spike sorting Modern analytical methods are far more sophisticated than our approach. In fact, spikes detected by multi-channel probes can be allocated to their neuron of origin on the basis of waveform shape (mostly amplitude), which varies as a function of distance and orientation from the probe (figure 4). The action potential outputs of hundreds of cells can, therefore, be simultaneously sampled and allocated to their neuron of origin. Analytical tools such as KiloSort <link http://biorxiv.org/content/early/2016/06/30/061481> now support the near real-time sorting of action potentials from hundreds of cells at once. Figure 4) Schematic showing how single action potentials from two cells can be allocated to their cell of origin. Left shows what a region of electrodes might detect in response to a single cell positioned near the probe. Right shows how two cells of slightly different positions might appear. The spatiotemporal pattern of events on different channels can then be used to group similar events that are likely to arise from the same cell. Joana sorted the spikes with KiloSort and found 62 different well-isolated neurons contributing to the data. An example of one of these identified clusters can be seen in the figure below.   Summary Overall for this part of the course we managed to acquire some exciting data – the first obtained by the lab with this configuration of the NeuroSeeker probe. We didn’t manage to test out our auditory experiments yet but we nonetheless have plenty of spontaneous data to tackle, which has allowed us to explore both rudimentary and state-of-the art analysis methods.