A real-time, event-driven neuromorphic system for goal-directed attentional selection

Francesco Galluppi; Kevin Brohan; Simon Davidson; Teresa Serrano-Gotarredona; José Antonio Pérez Carrasco; Bernabé Linares-Barranco; Stephen Furber

doi:10.1007/978-3-642-34481-7_28

A real-time, event-driven neuromorphic system for goal-directed attentional selection

Francesco Galluppi^*, Kevin Brohan, Simon Davidson, Teresa Serrano-Gotarredona, José Antonio Pérez Carrasco, Bernabé Linares-Barranco, Stephen Furber

^*Corresponding author for this work

Advanced Processor Technology

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Computation with spiking neurons takes advantage of the abstraction of action potentials into streams of stereotypical events, which encode information through their timing. This approach both reduces power consumption and alleviates communication bottlenecks. A number of such spiking custom mixed-signal address event representation (AER) chips have been developed in recent years. In this paper, we present i) a flexible event-driven platform consisting of the integration of a visual AER sensor and the SpiNNaker system, a programmable massively parallel digital architecture oriented to the simulation of spiking neural networks; ii) the implementation of a neural network for feature-based attentional selection on this platform.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	226-233
Number of pages	8
Volume	7664 LNCS
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-34481-7_28
Publication status	Published - 2012
Event	19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar Duration: 12 Nov 2012 → 15 Nov 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7664 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	19th International Conference on Neural Information Processing, ICONIP 2012
Country/Territory	Qatar
City	Doha
Period	12/11/12 → 15/11/12

Keywords

AER
Attention
Neuromorphic
Selection
SpiNNaker

Access to Document

10.1007/978-3-642-34481-7_28

Cite this

Galluppi, F., Brohan, K., Davidson, S., Serrano-Gotarredona, T., Carrasco, J. A. P., Linares-Barranco, B., & Furber, S. (2012). A real-time, event-driven neuromorphic system for goal-directed attentional selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 7664 LNCS, pp. 226-233). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7664 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-34481-7_28

Galluppi, Francesco ; Brohan, Kevin ; Davidson, Simon et al. / A real-time, event-driven neuromorphic system for goal-directed attentional selection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7664 LNCS PART 2. ed. 2012. pp. 226-233 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "Computation with spiking neurons takes advantage of the abstraction of action potentials into streams of stereotypical events, which encode information through their timing. This approach both reduces power consumption and alleviates communication bottlenecks. A number of such spiking custom mixed-signal address event representation (AER) chips have been developed in recent years. In this paper, we present i) a flexible event-driven platform consisting of the integration of a visual AER sensor and the SpiNNaker system, a programmable massively parallel digital architecture oriented to the simulation of spiking neural networks; ii) the implementation of a neural network for feature-based attentional selection on this platform.",

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Galluppi, F, Brohan, K, Davidson, S, Serrano-Gotarredona, T, Carrasco, JAP, Linares-Barranco, B & Furber, S 2012, A real-time, event-driven neuromorphic system for goal-directed attentional selection. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 7664 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7664 LNCS, pp. 226-233, 19th International Conference on Neural Information Processing, ICONIP 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34481-7_28

A real-time, event-driven neuromorphic system for goal-directed attentional selection. / Galluppi, Francesco; Brohan, Kevin; Davidson, Simon et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7664 LNCS PART 2. ed. 2012. p. 226-233 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7664 LNCS, No. PART 2).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AU - Carrasco, José Antonio Pérez

AU - Linares-Barranco, Bernabé

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Galluppi F, Brohan K, Davidson S, Serrano-Gotarredona T, Carrasco JAP, Linares-Barranco B et al. A real-time, event-driven neuromorphic system for goal-directed attentional selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 7664 LNCS. 2012. p. 226-233. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-34481-7_28

A real-time, event-driven neuromorphic system for goal-directed attentional selection

Abstract

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Keywords

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