Scalable software and models for large-scale extracellular recordings

The brain represents information about the world through the electrical activity of populations of neurons. By placing an electrode near a neuron that is firing (spiking), it is possible to detect the resulting extracellular action potential (EAP) that is transmitted down an axon to other neurons. In this way, it is possible to monitor the communication of a group of neurons to uncover how they encode and transmit information. As the number of recorded neurons continues to increase, however, so do the data processing and analysis challenges. It is crucial that scalable software and analysis tools are developed and made available to the neuroscience community to keep up with the large amounts of data that are already being gathered. This thesis is composed of three pieces of work which I develop in order to better process and analyze large-scale extracellular recordings. My work spans all stages of extracellular analysis from the processing of raw electrical recordings to the development of statistical models to reveal underlying structure in neural population activity. In the first work, I focus on developing software to improve the comparison and adoption of different computational approaches for spike sorting. When analyzing neural recordings, most researchers are interested in the spiking activity of individual neurons, which must be extracted from the raw electrical traces through a process called spike sorting. Much development has been directed towards improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, I develop SpikeInterface, an open-source, Python framework designed to unify preexisting spike sorting technologies into a single toolkit and to facilitate straightforward benchmarking of different approaches. With this framework, I demonstrate that modern, automated spike sorters have low agreement when analyzing the same dataset, i.e. they find different numbers of neurons with different activity profiles; This result holds true for a variety of simulated and real datasets. Also, I demonstrate that utilizing a consensus-based approach to spike sorting, where the outputs of multiple spike sorters are combined, can dramatically reduce the number of falsely detected neurons. In the second work, I focus on developing an unsupervised machine learning approach for determining the source location of individually detected spikes that are recorded by high-density, microelectrode arrays. By localizing the source of individual spikes, my method is able to determine the approximate position of the recorded neuriii ons in relation to the microelectrode array. To allow my model to work with large-scale datasets, I utilize deep neural networks, a family of machine learning algorithms that can be trained to approximate complicated functions in a scalable fashion. I evaluate my method on both simulated and real extracellular datasets, demonstrating that it is more accurate than other commonly used methods. Also, I show that location estimates for individual spikes can be utilized to improve the efficiency and accuracy of spike sorting. After training, my method allows for localization of one million spikes in approximately 37 seconds on a TITAN X GPU, enabling real-time analysis of massive extracellular datasets. In my third and final presented work, I focus on developing an unsupervised machine learning model that can uncover patterns of activity from neural populations associated with a behaviour being performed. Specifically, I introduce Targeted Neural Dynamical Modelling (TNDM), a statistical model that jointly models the neural activity and any external behavioural variables. TNDM decomposes neural dynamics (i.e. temporal activity patterns) into behaviourally relevant and behaviourally irrelevant dynamics; the behaviourally relevant dynamics constitute all activity patterns required to generate the behaviour of interest while behaviourally irrelevant dynamics may be completely unrelated (e.g. other behavioural or brain states), or even related to behaviour execution (e.g. dynamics that are associated with behaviour generally but are not task specific). Again, I implement TNDM using a deep neural network to improve its scalability and expressivity. On synthetic data and on real recordings from the premotor (PMd) and primary motor cortex (M1) of a monkey performing a center-out reaching task, I show that TNDM is able to extract low-dimensional neural dynamics that are highly predictive of behaviour without sacrificing its fit to the neural data

Spikeforest, reproducible web-facing ground-truth validation of automated neural spike sorters

Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions

Search for Scalar Diphoton Resonances in the Mass Range 65−60065-600 GeV with the ATLAS Detector in pppp Collision Data at s\sqrt{s} = 8 TeVTeV

A search for scalar particles decaying via narrow resonances into two photons in the mass range 65–600 GeV is performed using $20.3\text{}\text{}{\mathrm{fb}}^{-1}$ of $\sqrt{s}=8\text{}\text{}\mathrm{TeV}$ $pp$ collision data collected with the ATLAS detector at the Large Hadron Collider. The recently discovered Higgs boson is treated as a background. No significant evidence for an additional signal is observed. The results are presented as limits at the 95% confidence level on the production cross section of a scalar boson times branching ratio into two photons, in a fiducial volume where the reconstruction efficiency is approximately independent of the event topology. The upper limits set extend over a considerably wider mass range than previous searches

Finska tingsdomares bedömningar av partsutlåtanden givna på plats i rätten eller via videokonferens

Professionals within the judicial system sometimes believe they can assess whether someone is lying or not based on cues such as body language and emotional expression. Research has, however, shown that this is impossible. The Finnish Supreme Court has also given rulings in accordance with this demonstrated fact. There has also been previous research on whether party or witness statements are assessed differently in court depending on whether they are given live, via videoconference, or via prerecorded video. In the present study, we investigated how a Finnish sample of district judges (N=47) assigned probative value to different variables concerning the statement or the statement giver, such as body language and emotional expression. We also investigated the connection between the judges’ beliefs about the relevance of body language and emotional expression and their preference for live statements or statements via videoconference. The judges reported assigning equal amounts of probative value to statements given live and statements given via videoconference. However, judges found it easier to detect deception live, and this preference correlated with how relevant they thought body language is when assessing the probative value of the statement. In other words, a slight bias to assess live statements more favorably than statements given via videoconference might still exist. More effort needs to be put into making judges and Supreme Courts aware of robust scientific results that have been the subject of decades of research, such as the fact that one cannot assess whether someone is lying or not based on cues such as body language