Effective Dimensionality Reduction for Visualizing Neural Dynamics by Laplacian Eigenmaps

With the development of neural recording technology, it has become possible to collect activities from hundreds or even thousands of neurons simultaneously. Visualization of neural population dynamics can help neuroscientists analyze large-scale neural activities efficiently. In this letter, Laplacian eigenmaps is applied to this task for the first time, and the experimental results show that the proposed method significantly outperforms the commonly used methods. This finding was confirmed by the systematic evaluation using nonhuman primate data, which contained the complex dynamics well suited for testing. According to our results, Laplacian eigenmaps is better than the other methods in various ways and can clearly visualize interesting biological phenomena related to neural dynamics.

Download Full-text

A Universal Nonparametric Event Detection Framework for Neuropixels Data

10.1101/650671 ◽

2019 ◽

Author(s):

Hao Chen ◽

Shizhe Chen ◽

Xinyi Deng

Keyword(s):

Change Point ◽

Large Scale ◽

Brain Regions ◽

General Purpose ◽

Electrophysiological Recording ◽

Neural Population ◽

Data Sets ◽

Analysis Tool ◽

Neural Activities ◽

Spontaneous Behavior

SummaryNeuropixels probes present exciting new opportunities for neuroscience, but such large-scale high-density recordings also introduce unprecedented challenges in data analysis. Neuropixels data usually consist of hundreds or thousands of long stretches of sequential spiking activities that evolve non-stationarily over time and are often governed by complex, unknown dynamics. Extracting meaningful information from the Neuropixels recordings is a non-trial task. Here we introduce a general-purpose, graph-based statistical framework that, without imposing any parametric assumptions, detects points in time at which population spiking activity exhibits simultaneous changes as well as changes that only occur in a subset of the neural population, referred to as “change-points”. The sequence of change-point events can be interpreted as a footprint of neural population activities, which allows us to relate behavior to simultaneously recorded high-dimensional neural activities across multiple brain regions. We demonstrate the effectiveness of our method with an analysis of Neuropixels recordings during spontaneous behavior of an awake mouse in darkness. We observe that change-point dynamics in some brain regions display biologically interesting patterns that hint at functional pathways, as well as temporally-precise coordination with behavioral dynamics. We hypothesize that neural activities underlying spontaneous behavior, though distributed brainwide, show evidences for network modularity. Moreover, we envision the proposed framework to be a useful off-the-shelf analysis tool to the neuroscience community as new electrophysiological recording techniques continue to drive an explosive proliferation in the number and size of data sets.

Download Full-text

Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-scale Nutrition Study

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3397327 ◽

2020 ◽

Vol 4 (2) ◽

pp. 1-25

Author(s):

Jisu Jung ◽

Lyndal Wellard-Cole ◽

Colin Cai ◽

Irena Koprinska ◽

Kalina Yacef ◽

...

Keyword(s):

Large Scale ◽

Systematic Evaluation ◽

Nutrition Study

Download Full-text

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

Science Advances ◽

10.1126/sciadv.1601335 ◽

2016 ◽

Vol 2 (11) ◽

pp. e1601335 ◽

Cited By ~ 76

Author(s):

Jorge F. Mejias ◽

John D. Murray ◽

Henry Kennedy ◽

Xiao-Jing Wang

Keyword(s):

Large Scale ◽

Complex Dynamics ◽

Predictive Coding ◽

Causality Analysis ◽

Frequency Dependent ◽

Brain Functions ◽

Wide Range ◽

Cortical Laminar ◽

Feedback Frequency ◽

Visual Cortical

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

Download Full-text

Stability of slender inverted flags and rods in uniform steady flow

Journal of Fluid Mechanics ◽

10.1017/jfm.2016.691 ◽

2016 ◽

Vol 809 ◽

pp. 873-894 ◽

Cited By ~ 17

Author(s):

John E. Sader ◽

Cecilia Huertas-Cerdeira ◽

Morteza Gharib

Keyword(s):

Multiple Equilibria ◽

Complex Dynamics ◽

Flow Direction ◽

Flow Speed ◽

Uniform Flow ◽

Biological Phenomena ◽

Cantilever Length ◽

The Stability ◽

Elastic Sheets ◽

Clamped Edge

Cantilevered elastic sheets and rods immersed in a steady uniform flow are known to undergo instabilities that give rise to complex dynamics, including limit cycle behaviour and chaotic motion. Recent work has examined their stability in an inverted configuration where the flow impinges on the free end of the cantilever with its clamped edge downstream: this is commonly referred to as an ‘inverted flag’. Theory has thus far accurately captured the stability of wide inverted flags only, i.e. where the dimension of the clamped edge exceeds the cantilever length; the latter is aligned in the flow direction. Here, we theoretically examine the stability of slender inverted flags and rods under steady uniform flow. In contrast to wide inverted flags, we show that slender inverted flags are never globally unstable. Instead, they exhibit bifurcation from a state that is globally stable to multiple equilibria of varying stability, as flow speed increases. This theory is compared with new and existing measurements on slender inverted flags and rods, where excellent agreement is observed. The findings of this study have significant implications to investigations of biological phenomena such as the motion of leaves and hairs, which can naturally exhibit a slender geometry with an inverted configuration.

Download Full-text

Systematische Bewertung von FKV-Fertigungstechnologien*/Systematic evaluation of FRP manufacturing technologies - Technology evaluation by the example of large-scale series production of fiber-reinforced plastic (FRP) components

wt Werkstattstechnik online ◽

10.37544/1436-4980-2016-03-29 ◽

2016 ◽

Vol 106 (03) ◽

pp. 125-130

Author(s):

D. Hofbauer ◽

J. Greitemann ◽

M. Grammer ◽

J. Kaufmann ◽

G. Prof. Reinhart

Keyword(s):

Mass Production ◽

High Performance ◽

Large Scale ◽

Systematic Evaluation ◽

Series Production ◽

Fiber Reinforced ◽

Fiber Reinforced Plastic ◽

Reinforced Plastic ◽

Plastic Components ◽

Manufacturing Technologies

Hochleistungswerkstoffe wurden bisher nur für Spezialanwendungen eingesetzt, da hohe Materialkosten und eine geringe Reife der Fertigungstechnologien die Anwendung in der Großserie erschwert haben. Um die grundlegende Eignung der Technologien unter Beachtung der Produktanforderungen zu ermitteln, präsentiert dieser Fachbeitrag eine Methodik für die systematische Bewertung, die am Beispiel der Großserienfertigung von Bauteilen aus Faser-Kunststoff-Verbundwerkstoffen (FKV) erläutert wird.   The use of high-performance materials has so far been limited to special applications for reasons of high material costs and low maturity of manufacturing technologies. These facts avoided their use in mass production in the past. This paper presents a method for systematically evaluating technologies to determine their fundamental suitability for mass production. It is exemplified by large-scale series production of fiber-reinforced plastic components.

Download Full-text

Reaction Diffusion and Chemotaxis for Decentralized Gathering on FPGAs

International Journal of Reconfigurable Computing ◽

10.1155/2009/639249 ◽

2009 ◽

Vol 2009 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Bernard Girau ◽

César Torres-Huitzil ◽

Nikolaos Vlassopoulos ◽

José Hugo Barrón-Zambrano

Keyword(s):

Large Scale ◽

Complex Dynamics ◽

Diffusion Mechanism ◽

Reaction Diffusion ◽

Fpga Implementation ◽

Cellular Slime Mold ◽

Reaction Diffusion Model ◽

Cellular Slime ◽

Computational Resources ◽

Embedded Implementation

We consider here the feasibility of gathering multiple computational resources by means of decentralized and simple local rules. We study such decentralized gathering by means of a stochastic model inspired from biology: the aggregation of theDictyostelium discoideumcellular slime mold. The environment transmits information according to a reaction-diffusion mechanism and the agents move by following excitation fronts. Despite its simplicity this model exhibits interesting properties of self-organization and robustness to obstacles. We first describe the FPGA implementation of the environment alone, to perform large scale and rapid simulations of the complex dynamics of this reaction-diffusion model. Then we describe the FPGA implementation of the environment together with the agents, to study the major challenges that must be solved when designing a fast embedded implementation of the decentralized gathering model. We analyze the results according to the different goals of these hardware implementations.

Download Full-text

Neural Dynamics in Real-Time for Large Scale Biomorphic Neural Networks

ICANN 98 - Perspectives in Neural Computing ◽

10.1007/978-1-4471-1599-1_72 ◽

1998 ◽

pp. 481-486 ◽

Cited By ~ 2

Author(s):

M. Rossmann ◽

C. Burwick ◽

A. Bühlmeier ◽

G. Manteuffel ◽

K. Goser

Keyword(s):

Neural Networks ◽

Real Time ◽

Large Scale ◽

Neural Dynamics

Download Full-text

Automatic detection of Long Method and God Class code smells through neural source code embeddings

10.36227/techrxiv.17206010.v1 ◽

2021 ◽

Author(s):

Aleksandar Kovačević ◽

Jelena Slivka ◽

Dragan Vidaković ◽

Katarina-Glorija Grujić ◽

Nikola Luburić ◽

...

Keyword(s):

Machine Learning ◽

Large Scale ◽

Negative Impact ◽

Source Code ◽

Systematic Evaluation ◽

Small Scale ◽

Code Smells ◽

Code Metrics ◽

Code Smell ◽

F Measure

Code smells are structures in code that often have a negative impact on its quality. Manually detecting code smells is challenging and researchers proposed many automatic code smell detectors. Most of the studies propose detectors based on code metrics and heuristics. However, these studies have several limitations, including evaluating the detectors using small-scale case studies and an inconsistent experimental setting. Furthermore, heuristic-based detectors suffer from limitations that hinder their adoption in practice. Thus, researchers have recently started experimenting with machine learning (ML) based code smell detection. This paper compares the performance of multiple ML-based code smell detection models against multiple traditionally employed metric-based heuristics for detection of God Class and Long Method code smells. We evaluate the effectiveness of different source code representations for machine learning: traditionally used code metrics and code embeddings (code2vec, code2seq, and CuBERT). We perform our experiments on the large-scale, manually labeled MLCQ dataset. We consider the binary classification problem – we classify the code samples as smelly or non-smelly and use the F1-measure of the minority (smell) class as a measure of performance. In our experiments, the ML classifier trained using CuBERT source code embeddings achieved the best performance for both God Class (F-measure of 0.53) and Long Method detection (F-measure of 0.75). With the help of a domain expert, we perform the error analysis to discuss the advantages of the CuBERT approach. This study is the first to evaluate the effectiveness of pre-trained neural source code embeddings for code smell detection to the best of our knowledge. A secondary contribution of our study is the systematic evaluation of the effectiveness of multiple heuristic-based approaches on the same large-scale, manually labeled MLCQ dataset.

Download Full-text

Dynamics of the Auditory Continuity Illusion

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.676637 ◽

2021 ◽

Vol 15 ◽

Author(s):

Qianyi Cao ◽

Noah Parks ◽

Joshua H. Goldwyn

Keyword(s):

Firing Rate ◽

Noise Burst ◽

Auditory Scene Analysis ◽

Quantitative Model ◽

Neural Dynamics ◽

Sufficient Evidence ◽

Neural Population ◽

Neural Basis ◽

Continuous Responses ◽

Conceptual Explanations

Illusions give intriguing insights into perceptual and neural dynamics. In the auditory continuity illusion, two brief tones separated by a silent gap may be heard as one continuous tone if a noise burst with appropriate characteristics fills the gap. This illusion probes the conditions under which listeners link related sounds across time and maintain perceptual continuity in the face of sudden changes in sound mixtures. Conceptual explanations of this illusion have been proposed, but its neural basis is still being investigated. In this work we provide a dynamical systems framework, grounded in principles of neural dynamics, to explain the continuity illusion. We construct an idealized firing rate model of a neural population and analyze the conditions under which firing rate responses persist during the interruption between the two tones. First, we show that sustained inputs and hysteresis dynamics (a mismatch between tone levels needed to activate and inactivate the population) can produce continuous responses. Second, we show that transient inputs and bistable dynamics (coexistence of two stable firing rate levels) can also produce continuous responses. Finally, we combine these input types together to obtain neural dynamics consistent with two requirements for the continuity illusion as articulated in a well-known theory of auditory scene analysis: responses persist through the noise-filled gap if noise provides sufficient evidence that the tone continues and if there is no evidence of discontinuities between the tones and noise. By grounding these notions in a quantitative model that incorporates elements of neural circuits (recurrent excitation, and mutual inhibition, specifically), we identify plausible mechanisms for the continuity illusion. Our findings can help guide future studies of neural correlates of this illusion and inform development of more biophysically-based models of the auditory continuity illusion.

Download Full-text

Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review

Journal of Antimicrobial Chemotherapy ◽

10.1093/jac/dkaa257 ◽

2020 ◽

Vol 75 (11) ◽

pp. 3099-3108

Author(s):

Norhan Mahfouz ◽

Inês Ferreira ◽

Stephan Beisken ◽

Arndt von Haeseler ◽

Andreas E Posch

Keyword(s):

Antimicrobial Resistance ◽

Clinical Microbiology ◽

Large Scale ◽

Diagnostic Testing ◽

Error Rates ◽

Systematic Evaluation ◽

Large Scale Assessment ◽

Performance Requirements ◽

Public Repositories ◽

The Right

Abstract Background Antimicrobial resistance (AMR) is a rising health threat with 10 million annual casualties estimated by 2050. Appropriate treatment of infectious diseases with the right antibiotics reduces the spread of antibiotic resistance. Today, clinical practice relies on molecular and PCR techniques for pathogen identification and culture-based antibiotic susceptibility testing (AST). Recently, WGS has started to transform clinical microbiology, enabling prediction of resistance phenotypes from genotypes and allowing for more informed treatment decisions. WGS-based AST (WGS-AST) depends on the detection of AMR markers in sequenced isolates and therefore requires AMR reference databases. The completeness and quality of these databases are material to increase WGS-AST performance. Methods We present a systematic evaluation of the performance of publicly available AMR marker databases for resistance prediction on clinical isolates. We used the public databases CARD and ResFinder with a final dataset of 2587 isolates across five clinically relevant pathogens from PATRIC and NDARO, public repositories of antibiotic-resistant bacterial isolates. Results CARD and ResFinder WGS-AST performance had an overall balanced accuracy of 0.52 (±0.12) and 0.66 (±0.18), respectively. Major error rates were higher in CARD (42.68%) than ResFinder (25.06%). However, CARD showed almost no very major errors (1.17%) compared with ResFinder (4.42%). Conclusions We show that AMR databases need further expansion, improved marker annotations per antibiotic rather than per antibiotic class and validated multivariate marker panels to achieve clinical utility, e.g. in order to meet performance requirements such as provided by the FDA for clinical microbiology diagnostic testing.

Download Full-text