Intrinsic and extrinsic implementation of a bio-inspired hardware system

B Glackin; LP Maguire; Martin McGinnity

doi:10.1016/j.ins.2003.03.008

Intrinsic and extrinsic implementation of a bio-inspired hardware system

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	1-19
Journal	Information Sciences (Special Issue on Bio-Inspired Systems)
Volume	161
Issue number	1-2
DOIs	https://doi.org/10.1016/j.ins.2003.03.008
Publication status	Published - 5 Apr 2004

Bibliographical note

Other Details
------------------------------------
The paper developed a bio-inspired approach to implement neural networks architectures on FPGA hardware that is motivated by knowledge of the early years of a child's brain development. The paper extended a number of earlier conference publications by the authors. Work in this area enabled the authors to establish collaboration with Neuroscientists in France and subsequently to secure the EU-funded project SenseMaker - a multi-sensory, task-specific adaptable perception system (IST-2001-34712) that was led by McGinnity and Maguire. This publication has provided a focus for the Intelligent Systems research group's recent work on large scale implementations of spiking neural networks.

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10.1016/j.ins.2003.03.008

Cite this

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title = "Intrinsic and extrinsic implementation of a bio-inspired hardware system",

author = "B Glackin and LP Maguire and Martin McGinnity",

note = "Other Details ------------------------------------ The paper developed a bio-inspired approach to implement neural networks architectures on FPGA hardware that is motivated by knowledge of the early years of a child's brain development. The paper extended a number of earlier conference publications by the authors. Work in this area enabled the authors to establish collaboration with Neuroscientists in France and subsequently to secure the EU-funded project SenseMaker - a multi-sensory, task-specific adaptable perception system (IST-2001-34712) that was led by McGinnity and Maguire. This publication has provided a focus for the Intelligent Systems research group's recent work on large scale implementations of spiking neural networks.",

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