Details
| Original language | English |
|---|---|
| Title of host publication | 2012 International Joint Conference on Neural Networks, IJCNN 2012 |
| Publication status | Published - 2012 |
| Externally published | Yes |
| Event | 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, Australia Duration: 10 Jun 2012 → 15 Jun 2012 |
Publication series
| Name | Proceedings of the International Joint Conference on Neural Networks |
|---|
Abstract
In this work, we present a neurocomputational model for auditory-cue fear acquisition. Computational fear conditioning has experienced a growing interest over the last few years, on the one hand, because it is a robust and quick learning paradigm that can contribute to the development of more versatile robots, and on the other hand, because it can help in the understanding of fear conditioning and dysfunctions in animals. Fear learning involves sensory and motor aspects [1] and it is essential for adaptive self-protective systems. We argue that a deeper study of the mechanisms underlying fear circuits in the brain will contribute not only to the development of safer robots but eventually also to a better conceptual understanding of neural fear processing in general. Towards the development of a robotic adaptive self-protective system, we have designed a neural model of fear conditioning based on LeDoux's dual-route hypothesis of fear [2] and also dopamine modulated Pavlovian conditioning [3]. Our hybrid approach is capable of learning the temporal relationship between auditory sensory cues and an aversive or appetitive stimulus. The model was tested as a neural network simulation but it was designed to be used with minor modifications on a robotic platform.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
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2012 International Joint Conference on Neural Networks, IJCNN 2012. 2012. 6252392 (Proceedings of the International Joint Conference on Neural Networks).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A neurocomputational amygdala model of auditory fear conditioning
T2 - 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
AU - Navarro-Guerrero, Nicolas
AU - Lowe, Robert
AU - Wermter, Stefan
PY - 2012
Y1 - 2012
N2 - In this work, we present a neurocomputational model for auditory-cue fear acquisition. Computational fear conditioning has experienced a growing interest over the last few years, on the one hand, because it is a robust and quick learning paradigm that can contribute to the development of more versatile robots, and on the other hand, because it can help in the understanding of fear conditioning and dysfunctions in animals. Fear learning involves sensory and motor aspects [1] and it is essential for adaptive self-protective systems. We argue that a deeper study of the mechanisms underlying fear circuits in the brain will contribute not only to the development of safer robots but eventually also to a better conceptual understanding of neural fear processing in general. Towards the development of a robotic adaptive self-protective system, we have designed a neural model of fear conditioning based on LeDoux's dual-route hypothesis of fear [2] and also dopamine modulated Pavlovian conditioning [3]. Our hybrid approach is capable of learning the temporal relationship between auditory sensory cues and an aversive or appetitive stimulus. The model was tested as a neural network simulation but it was designed to be used with minor modifications on a robotic platform.
AB - In this work, we present a neurocomputational model for auditory-cue fear acquisition. Computational fear conditioning has experienced a growing interest over the last few years, on the one hand, because it is a robust and quick learning paradigm that can contribute to the development of more versatile robots, and on the other hand, because it can help in the understanding of fear conditioning and dysfunctions in animals. Fear learning involves sensory and motor aspects [1] and it is essential for adaptive self-protective systems. We argue that a deeper study of the mechanisms underlying fear circuits in the brain will contribute not only to the development of safer robots but eventually also to a better conceptual understanding of neural fear processing in general. Towards the development of a robotic adaptive self-protective system, we have designed a neural model of fear conditioning based on LeDoux's dual-route hypothesis of fear [2] and also dopamine modulated Pavlovian conditioning [3]. Our hybrid approach is capable of learning the temporal relationship between auditory sensory cues and an aversive or appetitive stimulus. The model was tested as a neural network simulation but it was designed to be used with minor modifications on a robotic platform.
UR - http://www.scopus.com/inward/record.url?scp=84865098665&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2012.6252392
DO - 10.1109/IJCNN.2012.6252392
M3 - Conference contribution
AN - SCOPUS:84865098665
SN - 9781467314909
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012
Y2 - 10 June 2012 through 15 June 2012
ER -