2,740 research outputs found
Measuring spike train synchrony
Estimating the degree of synchrony or reliability between two or more spike
trains is a frequent task in both experimental and computational neuroscience.
In recent years, many different methods have been proposed that typically
compare the timing of spikes on a certain time scale to be fixed beforehand.
Here, we propose the ISI-distance, a simple complementary approach that
extracts information from the interspike intervals by evaluating the ratio of
the instantaneous frequencies. The method is parameter free, time scale
independent and easy to visualize as illustrated by an application to real
neuronal spike trains obtained in vitro from rat slices. In a comparison with
existing approaches on spike trains extracted from a simulated Hindemarsh-Rose
network, the ISI-distance performs as well as the best time-scale-optimized
measure based on spike timing.Comment: 11 pages, 13 figures; v2: minor modifications; v3: minor
modifications, added link to webpage that includes the Matlab Source Code for
the method (http://inls.ucsd.edu/~kreuz/Source-Code/Spike-Sync.html
Translating Video Recordings of Mobile App Usages into Replayable Scenarios
Screen recordings of mobile applications are easy to obtain and capture a
wealth of information pertinent to software developers (e.g., bugs or feature
requests), making them a popular mechanism for crowdsourced app feedback. Thus,
these videos are becoming a common artifact that developers must manage. In
light of unique mobile development constraints, including swift release cycles
and rapidly evolving platforms, automated techniques for analyzing all types of
rich software artifacts provide benefit to mobile developers. Unfortunately,
automatically analyzing screen recordings presents serious challenges, due to
their graphical nature, compared to other types of (textual) artifacts. To
address these challenges, this paper introduces V2S, a lightweight, automated
approach for translating video recordings of Android app usages into replayable
scenarios. V2S is based primarily on computer vision techniques and adapts
recent solutions for object detection and image classification to detect and
classify user actions captured in a video, and convert these into a replayable
test scenario. We performed an extensive evaluation of V2S involving 175 videos
depicting 3,534 GUI-based actions collected from users exercising features and
reproducing bugs from over 80 popular Android apps. Our results illustrate that
V2S can accurately replay scenarios from screen recordings, and is capable of
reproducing 89% of our collected videos with minimal overhead. A case
study with three industrial partners illustrates the potential usefulness of
V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software
Engineering (ICSE'20), 13 page
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Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models
In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli
An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes
For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes
Review of Cetacean's click detection algorithms
The detection of echolocation clicks is key in understanding the intricate
behaviors of cetaceans and monitoring their populations. Cetacean species
relying on clicks for navigation, foraging and even communications are sperm
whales (Physeter macrocephalus) and a variety of dolphin groups. Echolocation
clicks are wideband signals of short duration that are often emitted in
sequences of varying inter-click-intervals. While datasets and models for
clicks exist, the detection and classification of clicks present a significant
challenge, mostly due to the diversity of clicks' structures, overlapping
signals from simultaneously emitting animals, and the abundance of noise
transients from, for example, snapping shrimps and shipping cavitation noise.
This paper provides a survey of the many detection and classification
methodologies of clicks, ranging from 2002 to 2023. We divide the surveyed
techniques into categories by their methodology. Specifically, feature analysis
(e.g., phase, ICI and duration), frequency content, energy based detection,
supervised and unsupervised machine learning, template matching and adaptive
detection approaches. Also surveyed are open access platforms for click
detections, and databases openly available for testing. Details of the method
applied for each paper are given along with advantages and limitations, and for
each category we analyze the remaining challenges. The paper also includes a
performance comparison for several schemes over a shared database. Finally, we
provide tables summarizing the existing detection schemes in terms of
challenges address, methods, detection and classification tools applied,
features used and applications.Comment: 23 pages, 6 tables, 4 figure
Speech-Based Blood Pressure Estimation with Enhanced Optimization and Incremental Clustering
Blood Pressure (BP) estimation plays a pivotal role in diagnosing various
health conditions, highlighting the need for innovative approaches to overcome
conventional measurement challenges. Leveraging machine learning and speech
signals, this study investigates accurate BP estimation with a focus on
preprocessing, feature extraction, and real-time applications. An advanced
clustering-based strategy, incorporating the k-means algorithm and the proposed
Fact-Finding Instructor optimization algorithm, is introduced to enhance
accuracy. The combined outcome of these clustering techniques enables robust BP
estimation. Moreover, extending beyond these insights, this study delves into
the dynamic realm of contemporary digital content consumption. Platforms like
YouTube have emerged as influential spaces, presenting an array of videos that
evoke diverse emotions. From heartwarming and amusing content to intense
narratives, YouTube captures a spectrum of human experiences, influencing
information access and emotional engagement. Within this context, this research
investigates the interplay between YouTube videos and physiological responses,
particularly Blood Pressure (BP) levels. By integrating advanced BP estimation
techniques with the emotional dimensions of YouTube videos, this study enriches
our understanding of how modern media environments intersect with health
implications.Comment: 29 pages, 2 tables, 9 figure
Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis
Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametri-zation of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.Instituto de Física de Líquidos y Sistemas Biológico
Speech Processing in Computer Vision Applications
Deep learning has been recently proven to be a viable asset in determining features in the field of Speech Analysis. Deep learning methods like Convolutional Neural Networks facilitate the expansion of specific feature information in waveforms, allowing networks to create more feature dense representations of data. Our work attempts to address the problem of re-creating a face given a speaker\u27s voice and speaker identification using deep learning methods. In this work, we first review the fundamental background in speech processing and its related applications. Then we introduce novel deep learning-based methods to speech feature analysis. Finally, we will present our deep learning approaches to speaker identification and speech to face synthesis. The presented method can convert a speaker audio sample to an image of their predicted face. This framework is composed of several chained together networks, each with an essential step in the conversion process. These include Audio embedding, encoding, and face generation networks, respectively. Our experiments show that certain features can map to the face and that with a speaker\u27s voice, DNNs can create their face and that a GUI could be used in conjunction to display a speaker recognition network\u27s data
A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming
Real-time and long-term behavioural monitoring systems in precision livestock farming have huge potential to improve welfare and productivity for the better health of farm animals. However, some of the biggest challenges for long-term monitoring systems relate to “concept drift”, which occurs when systems are presented with challenging new or changing conditions, and/or in scenarios where training data is not accurately reflective of live sensed data. This study presents a combined offline algorithm and online learning algorithm which deals with concept drift and is deemed by the authors as a useful mechanism for long-term in-the-field monitoring systems. The proposed algorithm classifies three relevant sheep behaviours using information from an embedded edge device that includes tri-axial accelerometer and tri-axial gyroscope sensors. The proposed approach is for the first time reported in precision livestock behavior monitoring and demonstrates improvement in classifying relevant behaviour in sheep, in real-time, under dynamically changing conditions
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