Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator

Crone, Joshua C.; Vindiola, Manuel M.; Yu, Alfred B.; Boothe, David L.; Beeman, David; Oie, Kelvin S.; Franaszczuk, Piotr J.

doi:10.3389/fninf.2019.00069

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…In the compartmental approach, neuron's compartments in GENESIS are linked to their ion channels, and the channels are linked together to form multi-compartmental neurons of up to 50–74 compartments per neuron. GENESIS simulations scale on super-computing resources to neural network sizes as large as 9 × 106 neurons with 18 × 109 synapses and 2.2 × 106 neurons with 45 × 109 synapses (Crone et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

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Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

Chlasta

Paweł

Wójcik

et al. 2023

Front. Neuroinform.

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In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running computer simulations but leaves a gap for setting up today's larger and more complex models. The field of realistic models of brain networks has overgrown the simplicity of earliest models. The challenges include managing the complexity of software dependencies and various models, setting up model parameter values, storing the input parameters alongside the results, and providing execution statistics. Moreover, in the high performance computing (HPC) context, public cloud resources are becoming an alternative to the expensive on-premises clusters. We present Neural Simulation Pipeline (NSP), which facilitates the large-scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as the code (IaC) containerization approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(8 × 5,1) that uses biologically plausible Hodgkin–Huxley spiking neurons. We evaluate the pipeline by performing 54 simulations executed on-premise, at the Hasso Plattner Institute's (HPI) Future Service-Oriented Computing (SOC) Lab, and through the Amazon Web Services (AWS), the biggest public cloud service provider in the world. We report on the non-containerized and containerized execution with Docker, as well as present the cost per simulation in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost-effective.

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Section: Methodsmentioning

confidence: 99%

“…This is due to both methodological and IT challenges (Eliasmith and Trujillo, 2014 ), which include maintaining software dependencies and executing on different hardware infrastructures. The task is not trivial from a technical perspective even when using as well-established simulation engine as GENESIS (Crone et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

Chlasta

Paweł

Wójcik

et al. 2023

Front. Neuroinform.

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“…For instance, SWC files can be easily loaded into NEURON. Once imported, each segment is represented as a cylinder as depicted in example is NeuroConstruct (Gleeson et al, 2007), in which not only it is possible to load different types of morphology files but also to generate scripts compatible with different simulators such as NEURON, GENESIS (Bower and Beeman, 2012;Crone et al, 2019), MOOSE (Bhalla, 2008;Ray et al, 2008), PSICS (Cannon et al, 2010) and PyNN (Davison et al, 2009). NEURON and other simulation environments will be better discussed in the next subsection.…”

Section: Connectivity Maps For Network Of Neurons With Morphologymentioning

confidence: 99%

Computational study of thalamocortical interactions: simulating oscillatory activity

Shimoura¹

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Primeiramente à minha família que sempre me apoiou em minhas conquistas e dificuldades. Aos meus pais Maria Emília e Tsuguio Shimoura pela educação e encourajamento sempre presentes. Ambos sempre orgulhosos de qualquer conquista que adquiri até o momento. Infelizmente meu pai não estará presente para a finalização de mais essa etapa de minha vida, que ele esteja descansando em paz. À minha irmãSuélen e cunhado Elton, por todo apoio e suporte. Aos meus tios e padrinhos Silvia e Hélio que sempre mantiveram o incentivo e a confiança em mim. A minha tia Gláucia, primos e primas, e também aos meus avós Maria Rosa e José. Todos importantíssimos em minha vida. Também à Letícia, minha companheira, que me suportou e me deu apoio na reta final desse trabalho. Aos meus amigos(as) espalhados pelo Brasil e pelo mundo, sejam eles(as) amigos(as) adquiridos(as) na infância, no ensino médio, na faculdade, no laboratório e nos eventos acadêmicos, ou pelas situações da vida. Sem citar nomes, agradeço a todos, quem ler saberá que está incluído. Já peço desculpas de antemão, mas sou péssimo em listar esse tipo de coisa e para evitar esquecer alguém, prefiro manter assim. Apenas saibam que foram importantíssimos para a conclusão desse trabalho, seja pela ajuda acadêmica ou pelas distrações que me mantiveram mentalmente saudável dentro do possível. Ao meu orientador, Prof. Dr. Antônio Carlos Roque por todo apoio e orientação dados nesses últimos anos. Agradeço também pela confiança, respeito e, principalmente, pelo incentivo sempre presente nas apresentações/participações em diferentes congressos, cursos e workshops, experiência essa da qual foi grande valia para meu desenvolvimento como pesquisador. To the Institute of Neuroscience and Medicine -Computational and Systems Neuroscience (INM-6 / IAS-6) from Forschungszentrum Jülich where I did my v vi internship in Germany. My thanks to all people from the institute, specially to my supervisors Prof. Dr. Markus Diesmann and Dr. Sacha J. van Albada for the guidance, encouragement, patience and kindness which they received me.À FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo) pelo financiamento de minha pesquisa de Doutorado no país (bolsa de doutorado, processo nº 2017/07688-9; e Bolsa Estágio de Pesquisa no Exterior (BEPE), processo nº 2018/08556-1).

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Impact of neural cyberattacks on a realistic neuronal topology from the primary visual cortex of mice

López Madejska,

López Bernal,

Martínez Pérez

et al. 2024

Wireless Netw

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Brain-computer interfaces (BCIs) are widely used in medical scenarios to treat neurological conditions, such as Parkinson’s disease or epilepsy, when a pharmacological approach is ineffective. Despite their advantages, these BCIs target relatively large areas of the brain, causing side effects. In this context, projects such as Neuralink aim to stimulate and inhibit neural activity with single-neuron resolution, expand their usage to other sectors, and thus democratize access to neurotechnology. However, these initiatives present vulnerabilities in their designs that cyberattackers can exploit to cause brain damage. Specifically, the literature has documented the applicability of neural cyberattacks, threats capable of stimulating or inhibiting individual neurons to alter spontaneous neural activity. However, these works were limited by a lack of realistic neuronal topologies to test the cyberattacks. Surpassed this limitation, this work considers a realistic neuronal representation of the primary visual cortex of mice to evaluate the impact of neural cyberattacks more realistically. For that, this publication evaluates two existing cyberattacks, Neuronal Flooding and Neuronal Jamming, assessing the impact that different voltages on a particular set of neurons and the number of neurons simultaneously under attack have on the amount of neural activity produced. As a result, both cyberattacks increased the number of neural activations, propagating their impact for approximately 600 ms, where the activity converged into spontaneous behavior. These results align with current evidence about the brain, highlighting that neurons will tend to their baseline behavior after the attack.

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Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator

Cited by 5 publications

References 35 publications

Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds

Computational study of thalamocortical interactions: simulating oscillatory activity

Impact of neural cyberattacks on a realistic neuronal topology from the primary visual cortex of mice

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