Think: Theory for Africa

Currin, Christopher Brian; Khoza, Phumlani Nhlanganiso; Antrobus, Alexander D.; Latham, Peter E.; Vogels, Tim P.; Raimondo, Joseph V.

doi:10.1371/journal.pcbi.1007049

Cited by 5 publications

(4 citation statements)

References 15 publications

(12 reference statements)

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“…In our hands the speed-up factor for calibrating a network of 25, 000 neurons for two independent parameters was on the order of 10 − 20. Even a low-budget GPU allows to conduct scientific projects with networks of considerable size, which may be specifically usefull for teaching and training purposes (Currin et al, 2019). To our experience, and in comparison to NEST, GeNN requires more advanced programming expertise.…”

Section: Discussionmentioning

confidence: 99%

Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST

Schmitt

Rostami

Nawrot

2022

Preprint

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Spiking neural networks (SNN) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters to achieve robust network function demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments, and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large scale and real-time SNN simulation. The widely used NEural Simulation Technology (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highlyparallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on different hardware configurations. As benchmark model we us a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters. We show that simulation time scales linear with the simulated biological time and, for large networks, approximately linear with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN for model compilation are independent of model size while fixed costs with NEST for model building increase linear with model size. We demonstrate how GeNN can be used for simulating networks with up to 3, 5 million neurons (> 3 × 1012 synapses) on a high-end GPU, and up to 250 thousand neurons (25 billion synapses) on a low-cost GPU. Real-time simulation was achieved for networks of more than 90 k neurons. We conclude that GeNN is in advantage for large networks and real-time applications while NEST plays out its strengths in a high degree of flexibility ideal for prototyping, and easy access for non-expert programmers.

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Section: Discussionmentioning

confidence: 99%

Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST

Schmitt

Rostami

Nawrot

2022

Preprint

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“…Unfortunately, African scientists are often sidelined by workshops conducted in other continents due to prohibitive travel costs and the challenge of navigating the labyrinth of travel restrictions imposed on many African nationals 20 . Likewise, there have been precious few training opportunities on the continent, despite efforts such as IBRO-Simons Computational Neuroscience Imbizo (ISi-CNI) 21 and Deep Learning Indaba 22 . With limited opportunities for this type of skill development, African life scientists, if given a chance to attend an international workshop, tend to gravitate toward techniques most commonly used in their research.…”

Section: Commentmentioning

confidence: 99%

“…As an educational initiative aimed at addressing the problems outlined in the previous section, Imaging Africa offered a continent-wide, fully funded microscopy boot camp that combined (i) theory-based lectures, (ii) practical learning on a range of microscopes, (iii) hands-on, quantitative digital image processing and analysis and (iv) a symposium on emerging imaging technologies. One comparable opportunity is the ISi-CNI 21 , which is conducted annually in Cape Town, South Africa, and is focused on, but not restricted to, students from Africa. Its curriculum centers around cutting-edge computational and theoretical neuroscience and is focused on training neuroscientists with complex data in realizing the full potential of their biological queries through advanced computational methods.…”

Section: Solutions and Approachesmentioning

confidence: 99%

Imaging Africa: a strategic approach to optical microscopy training in Africa

et al. 2021

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“…Within the past 3 years, the COVID-19 pandemic has seen increased focus placed on health and healthcare systems, with flaws and opportunities exposed. Africans represent a vast and growing intellectual capital developing ideas and solutions that are impactful locally and relevant globally [2]. These three aspects form a confluence of opportunities for applying the latest technological tools in a vital field within an intellectually rich but historically exploited context.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa

Turki¹,

Pouris²,

Ifeanyichukwu³

et al. 2023

Preprint

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Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

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Think: Theory for Africa

Cited by 5 publications

References 15 publications

Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST

Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST

Imaging Africa: a strategic approach to optical microscopy training in Africa

Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa

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