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Creator |
edddad9736f93e930a103c8bc63856c1 |
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Creator |
ef6859ec86ef4adb58c0255d78e856b1 |
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Date |
2017 |
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Is Part Of |
repository |
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Is Part Of |
p14333058 |
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abstract |
The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the
development of tools for the simulation and study of neuronal dynamics ranging from
phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based
and multi-compartmental models. However, despite the multiple features offered by
neural modelling tools, their integration with environments for the simulation of
robots and agents can be challenging and time consuming. The implementation of artificial
neural circuits to control robots generally involves the following tasks: (1) understanding
the simulation tools, (2) creating the neural circuit in the neural simulator, (3)
linking the simulated neural circuit with the environment of the agent and (4) programming
the appropriate interface in the robot or agent to use the neural controller. The
accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate
students or novice researchers. This paper presents an alternative tool which facilitates
the simulation of simple SNN circuits using the multi-agent simulation and the programming
environment Netlogo (educational software that simplifies the study and experimentation
of complex systems). The engine proposed and implemented in Netlogo for the simulation
of a functional model of SNN is a simplification of integrate and fire (I&F) models.
The characteristics of the engine (including neuronal dynamics, STDP learning and
synaptic delay) are demonstrated through the implementation of an agent representing
an artificial insect controlled by a simple neural circuit. The setup of the experiment
and its outcomes are described in this work. |
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authorList |
authors |
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issue |
Suppl 1 |
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status |
published |
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status |
peerReviewed |
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uri |
http://data.open.ac.uk/oro/document/641186 |
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uri |
http://data.open.ac.uk/oro/document/641199 |
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uri |
http://data.open.ac.uk/oro/document/643245 |
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uri |
http://data.open.ac.uk/oro/document/643253 |
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uri |
http://data.open.ac.uk/oro/document/643254 |
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uri |
http://data.open.ac.uk/oro/document/643255 |
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uri |
http://data.open.ac.uk/oro/document/643256 |
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uri |
http://data.open.ac.uk/oro/document/643257 |
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uri |
http://data.open.ac.uk/oro/document/666843 |
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volume |
28 |
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type |
AcademicArticle |
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type |
Article |
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label |
Jimenez-Romero, Cristian and Johnson, Jeffrey (2017). SpikingLab: modelling
agents controlled by Spiking Neural Networks in Netlogo. Neural Computing and Applications,
28(Suppl 1) pp. 755–764. |
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label |
Jimenez-Romero, Cristian and Johnson, Jeffrey (2017). SpikingLab: modelling agents
controlled by Spiking Neural Networks in Netlogo. Neural Computing and Applications,
28(Suppl 1) pp. 755–764. |
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Publisher |
ext-1954a6c4c17bbe1623d2bad57b598b33 |
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Title |
SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo |
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in dataset |
oro |