Improving data quality in neuronal population recordings

Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here, we discuss methods for assessing and improving the quality of data from these techniques, and outline likely future directions in this field

The Affine Uncertainty Principle, Associated Frames and Applications in Signal Processing

Uncertainty relations play a prominent role in signal processing, stating that a signal can not be simultaneously concentrated in the two related domains of the corresponding phase space. In particular, a new uncertainty principle for the affine group, which is directly related to the wavelet transform has lead to a new minimizing waveform. In this thesis, a frame construction is proposed which leads to approximately tight frames based on this minimizing waveform. Frame properties such as the diagonality of the frame operator as well as lower and upper frame bounds are analyzed. Additionally, three applications of such frame constructions are introduced: inpainting of missing audio data, detection of neuronal spikes in extracellular recorded data and peak detection in MALDI imaging data

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm

Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called "SpikeDeep-Classifier" is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that "SpikeDeep-Classifier" possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting

NeuSort: An Automatic Adaptive Spike Sorting Approach with Neuromorphic Models

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting

Algorithms for Neural Prosthetic Applications

abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

Challenges and opportunities for large-scale electrophysiology with Neuropixels probes

Electrophysiological methods are the gold standard in neuroscience because they reveal the activity of individual neurons at high temporal resolution and in arbitrary brain locations. Microelectrode arrays based on complementary metal-oxide semiconductor (CMOS) technology, such as Neuropixels probes, look set to transform these methods. Neuropixels probes provide ∼1000 recording sites on an extremely narrow shank, with on-board amplification, digitization, and multiplexing. They deliver low-noise recordings from hundreds of neurons, providing a step change in the type of data available to neuroscientists. Here we discuss the opportunities afforded by these probes for large-scale electrophysiology, the challenges associated with data processing and anatomical localization, and avenues for further improvements of the technology

Formation of Intracardiac Electrograms under Physiological and Pathological Conditions

This work presents methods to advance electrophysiological simulations of intracardiac electrograms (IEGM). An experimental setup is introduced, which combines electrical measurements of extracellular potentials with a method for optical acquisition of the transmembrane voltage in-vitro. Thereby, intracardiac electrograms can be recorded under defined conditions. Using experimental and clinical signals, detailed simulations of IEGMs are parametrized, which can support clinical diagnosis

Exploratory data analysis of microelectrode neural activity of the globus pallidus internus in dystonia

Jadrá bazálných ganglií majú dopad na riadenie pohybu pri dystónií, preto je potrebné tieto jadrá preskúmať. Cieľom bakalárskej práce je prieskumová analýza mikroelektrodovej aktivity neurónov v oblasti globus pallidus internus. Pre splnenie tohto cieľa je potrebné detekovať artefakty a navrhnúť výpočet vhodného popisujúceho príznaku v mikroelektródových záznamoch (mEEG) získaných peroperačne počas zavádzania hlbokej mozgovej stimulácie interného pallida u pacientov trpiacích dystóniou. Cieľ bol rozdelený na niekoľko podcieľov. Od konvertovania dostupných dát, cez filtráciu mEEG signálov až po automatickú detekciu artefaktov a výpočet popisujúceho príznaku. Na detekciu sme použili metódy podľa E. Bakšteina a kol. (2017), ktorí týmito metódami detekovali artefakty v mEEGu pacientov trpiacich Parkinsonovou chorobou. MER záznamy z GPi (Globus Pallidus interus) a STN (subtalamické jadro) majú podobnú genéziu a charakteristický tvar. Boli vytvorené offline skripty pre implementáciu všetkých fáz predspracovania. Definovaný cieľ sa podarilo splniť. Mikroelektródové záznamy boli očistené od artefaktov a následne boli v práci analyzované a štatisticky spracované. Na základe vypočítaného príznaku je možné určiť oblasť, kde sa GPi nachádza (pThe basal ganglia nuclei have an influence on motion control in dystonia, so it need to be explored. The aim of the bachelor thesis is an exploratory data analysis of microelectrode neural activity of the globus pallidus internus in dystonia. To achieve this goal, it is necessary to detect artifacts and to suggest the calculation of a suitable feature description in microelectrode recordings (mEEG) obtained peroperatively during the introduction of deep brain stimulation in patients suffering from dystonia. The aim was divided into several sub-aims. From converting available data, by filtering mEEG signals to automatic artifact detection, and computationally describing the biomarker. For detection, we used the methods of E. Bakstein et al. (2017) who detected these artifacts in mEEG in those patients with Parkinson's disease. MER recordings from GPi (Globus Pallidus internus) and STN (subthalamic nucleus) have similar genesis and a characteristic shape. Offline scripts have been created to implement all pre-processing phases. Microelectrode recordings were cleared from artefacts and subsequently analyzed and statistically processed. Based on the computed marker, it is possible to determine the area where the GPi is located (