Clustering: how much bias do we need?

Lorimer, Tom; Held, Jenny; Stoop, Ruedi

doi:10.1098/rsta.2016.0293

Cited by 12 publications

(14 citation statements)

References 31 publications

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“…To see how the avalanches are organized in this space, we used an unsupervised Hebbian learning clustering (HLC) developed by us [48][49][50][51] . This approach finds clusters of arbitrary shapes, without prior knowledge of the number of clusters or requiring a data dimensionality reduction step that generally distorts and biases the distances between data points 52 . Moreover, HLC has the capacity to filter out noise, by leaving such data without assignment to a cluster.…”

Section: Discussionmentioning

confidence: 99%

Fingerprints of a second order critical line in developing neural networks

et al. 2020

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Patterns of biological activity with properties similar to critical states of statistical mechanics have received much attention, as they were mostly seen as indicators of computational optimality. Commonly, a single regime around an isolated critical point is expected. Our experimental data and our network simulations of developing neural cultures indicate the possibility of transitions between different critical regimes. In the latter, the addition of further fundamental neurophysiological principles to the standard neurodynamics branching model generates steeper power laws that have been observed in various experiments. Our analysis exhibits two populations of neurons, each composed of inhibitory and excitatory sites, that have distinct dynamical and topological properties. This generates a line of second order critical points, similar to what is known from the thermodynamics of two-component alloys. An analysis of two major critical regimes found in the experiments suggests that different critical regimes may express distinct computational roles.

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Section: Discussionmentioning

confidence: 99%

Fingerprints of a second order critical line in developing neural networks

et al. 2020

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“…-better data analysis and diagnoses [9,12,16,17]; -better therapies or instrumentation [15,[19][20][21]; -better understanding of biological processes [10,13,14]; and -better understanding of medical disorders [11,18].…”

Section: Resultsmentioning

confidence: 99%

“…It also illustrates nicely how different mathematical subject areas usually concur in the modelling and analysis of a specific topic. For example, networks are used in a particular clustering algorithm [12], represent a symbolic time series [16], and are the framework of an infection spreading model [18].…”

Section: Resultsmentioning

confidence: 99%

“…Borrowing the definition from Lorimer et al [12], clustering is the partitioning of a set of objects into subsets, or clusters, that express, among themselves and compared to objects not in the cluster, an increased degree of similarity. As an example, think of the identification of malicious tissue on a positron emission tomography scan of a part of the body.…”

Section: (C) Data Processingmentioning

confidence: 99%

“…This theme issue on the applications of mathematics to medicine is composed of six papers belonging to the area of neuroscience [9][10][11][12][13][14], three papers belonging to cardiology [15][16][17] and four papers belonging to pathology [18][19][20][21], totalling 13 papers from collaborations among 55 scientists.…”

Section: This Issuementioning

confidence: 99%

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Mathematical methods in medicine: neuroscience, cardiology and pathology

Amigó

Small

2017

Phil. Trans. R. Soc. A.

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The application of mathematics, natural sciences and engineering to medicine is gaining momentum as the mutual benefits of this collaboration become increasingly obvious. This theme issue is intended to highlight the trend in the case of mathematics. Specifically, the scope of this theme issue is to give a general view of the current research in the application of mathematical methods to medicine, as well as to show how mathematics can help in such important aspects as understanding, prediction, treatment and data processing. To this end, three representative specialties have been selected: neuroscience, cardiology and pathology. Concerning the topics, the 12 research papers and one review included in this issue cover biofluids, cardiac and virus dynamics, computational neuroscience, functional magnetic resonance imaging data processing, neural networks, optimization of treatment strategies, time-series analysis and tumour growth. In conclusion, this theme issue contains a collection of fine contributions at the intersection of mathematics and medicine, not as an exercise in applied mathematics but as a multidisciplinary research effort that interests both communities and our society in general. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.

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Bias in data‐driven artificial intelligence systems—An introductory survey

Ntoutsi

Fafalios

Gadiraju

et al. 2020

WIREs Data Min & Knowl

536

259

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Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.This article is categorized under:

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Clustering: how much bias do we need?

Cited by 12 publications

References 31 publications

Fingerprints of a second order critical line in developing neural networks

Fingerprints of a second order critical line in developing neural networks

Mathematical methods in medicine: neuroscience, cardiology and pathology

Bias in data‐driven artificial intelligence systems—An introductory survey

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