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Survey and evaluation of neural computation models for bio-integrated systems

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Survey and evaluation of neural computation models for bio-integrated systems. / Christophe, Francois; Andalibi, Vafa; Laukkarinen, Teemu; Mikkonen, Tommi; Koskimies, Kai.

In: Nano Communication Networks, Vol. 6, No. 4, 12.2015, p. 155-165.

Research output: Contribution to journalReview ArticleScientificpeer-review

Harvard

Christophe, F, Andalibi, V, Laukkarinen, T, Mikkonen, T & Koskimies, K 2015, 'Survey and evaluation of neural computation models for bio-integrated systems', Nano Communication Networks, vol. 6, no. 4, pp. 155-165. https://doi.org/10.1016/j.nancom.2015.08.001

APA

Christophe, F., Andalibi, V., Laukkarinen, T., Mikkonen, T., & Koskimies, K. (2015). Survey and evaluation of neural computation models for bio-integrated systems. Nano Communication Networks, 6(4), 155-165. https://doi.org/10.1016/j.nancom.2015.08.001

Vancouver

Christophe F, Andalibi V, Laukkarinen T, Mikkonen T, Koskimies K. Survey and evaluation of neural computation models for bio-integrated systems. Nano Communication Networks. 2015 Dec;6(4):155-165. https://doi.org/10.1016/j.nancom.2015.08.001

Author

Christophe, Francois ; Andalibi, Vafa ; Laukkarinen, Teemu ; Mikkonen, Tommi ; Koskimies, Kai. / Survey and evaluation of neural computation models for bio-integrated systems. In: Nano Communication Networks. 2015 ; Vol. 6, No. 4. pp. 155-165.

Bibtex - Download

@article{53b599c86f774ddfa21263eb6c19ca6c,
title = "Survey and evaluation of neural computation models for bio-integrated systems",
abstract = "Integrating neurobiological cultures with computer systems presents an opportunity to enhance computational energy efficiency. These Bio-Integrated Systems (BISs) require knowledge about structure and behavior of neural components and their interfacing. In the early design phases, modeling neurons offers cost, failure-free and retrial benefits compared to laboratory grown neural networks. The usefulness of these models lays in characteristics of being realistic but also computationally efficient.This survey reviews computational models of spiking neurons and their changes in connections, known as plasticity. The review studies models that are faithful to real neural cultures, and are computational efficient for real-time BISs. Also, criteria and methods for comparing models with ‘in-vitro’ experiments are reviewed to conclude on the level of realism of models in comparison with biological setups. Izhikevich’s model of spiking neurons is recommended due to its accuracy in reproducing real neural firing patterns, computational efficiency, and ease of parameter adjustment. The model of Spike-timing dependent plasticity is recommended as current basis for representing neuron changes in connections. For the analysis of network connectivity and connectivity changes in BIS, the Cox method is recommended because it evaluates connections based on activities from all recorded neurons as opposed to pair-wise approaches.",
keywords = "Computational models of spiking neurons, Spike-timing dependent connectivity changes, Plasticity models, Spike-Timing dependent plasticity, Network connectivity analysis methods, Bio-integrated systems",
author = "Francois Christophe and Vafa Andalibi and Teemu Laukkarinen and Tommi Mikkonen and Kai Koskimies",
year = "2015",
month = "12",
doi = "10.1016/j.nancom.2015.08.001",
language = "English",
volume = "6",
pages = "155--165",
journal = "Nano Communication Networks",
issn = "1878-7789",
publisher = "Elsevier Science B.V.",
number = "4",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Survey and evaluation of neural computation models for bio-integrated systems

AU - Christophe, Francois

AU - Andalibi, Vafa

AU - Laukkarinen, Teemu

AU - Mikkonen, Tommi

AU - Koskimies, Kai

PY - 2015/12

Y1 - 2015/12

N2 - Integrating neurobiological cultures with computer systems presents an opportunity to enhance computational energy efficiency. These Bio-Integrated Systems (BISs) require knowledge about structure and behavior of neural components and their interfacing. In the early design phases, modeling neurons offers cost, failure-free and retrial benefits compared to laboratory grown neural networks. The usefulness of these models lays in characteristics of being realistic but also computationally efficient.This survey reviews computational models of spiking neurons and their changes in connections, known as plasticity. The review studies models that are faithful to real neural cultures, and are computational efficient for real-time BISs. Also, criteria and methods for comparing models with ‘in-vitro’ experiments are reviewed to conclude on the level of realism of models in comparison with biological setups. Izhikevich’s model of spiking neurons is recommended due to its accuracy in reproducing real neural firing patterns, computational efficiency, and ease of parameter adjustment. The model of Spike-timing dependent plasticity is recommended as current basis for representing neuron changes in connections. For the analysis of network connectivity and connectivity changes in BIS, the Cox method is recommended because it evaluates connections based on activities from all recorded neurons as opposed to pair-wise approaches.

AB - Integrating neurobiological cultures with computer systems presents an opportunity to enhance computational energy efficiency. These Bio-Integrated Systems (BISs) require knowledge about structure and behavior of neural components and their interfacing. In the early design phases, modeling neurons offers cost, failure-free and retrial benefits compared to laboratory grown neural networks. The usefulness of these models lays in characteristics of being realistic but also computationally efficient.This survey reviews computational models of spiking neurons and their changes in connections, known as plasticity. The review studies models that are faithful to real neural cultures, and are computational efficient for real-time BISs. Also, criteria and methods for comparing models with ‘in-vitro’ experiments are reviewed to conclude on the level of realism of models in comparison with biological setups. Izhikevich’s model of spiking neurons is recommended due to its accuracy in reproducing real neural firing patterns, computational efficiency, and ease of parameter adjustment. The model of Spike-timing dependent plasticity is recommended as current basis for representing neuron changes in connections. For the analysis of network connectivity and connectivity changes in BIS, the Cox method is recommended because it evaluates connections based on activities from all recorded neurons as opposed to pair-wise approaches.

KW - Computational models of spiking neurons

KW - Spike-timing dependent connectivity changes

KW - Plasticity models

KW - Spike-Timing dependent plasticity

KW - Network connectivity analysis methods

KW - Bio-integrated systems

UR - http://www.sciencedirect.com/science/article/pii/S1878778915000319

U2 - 10.1016/j.nancom.2015.08.001

DO - 10.1016/j.nancom.2015.08.001

M3 - Review Article

VL - 6

SP - 155

EP - 165

JO - Nano Communication Networks

JF - Nano Communication Networks

SN - 1878-7789

IS - 4

ER -