An advantage of chaotic neural dynamics

Andras, Peter; Lycett, Samantha

Abstract

One hypothesis about how biological neural systems work suggests that they use attractor dynamics to define their behaviour. Such behaviour can be modelled using recurrent neural network models. It has been shown that such systems can perform a wide range of computational tasks by learning abstract grammars. Here we show that chaotic neural dynamics in recurrent neural systems is advantageous in the sense that it facilitates the encoding of grammars describing complex behaviour. This result may explain why it is common the observation of chaotic dynamics in biological neural systems.

Citation

Andras, P., & Lycett, S. (2007, August). An advantage of chaotic neural dynamics. Presented at 2007 International Joint Conference on Neural Networks, Orlando, FL, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2007 International Joint Conference on Neural Networks
• Start Date: Aug 12, 2007
• End Date: Aug 17, 2007
• Online Publication Date: Oct 29, 2007
• Publication Date: 2007
• Deposit Date: Nov 18, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1417-1422
• Series ISSN: 2161-4393
• Book Title: 2007 International Joint Conference on Neural Networks
• ISBN: 978-1-4244-1379-9
• DOI: https://doi.org/10.1109/IJCNN.2007.4371166
• Keywords: Chaos, Neurons, Chaotic communication, Recurrent neural networks, Biological system modeling, State-space methods, Biological information theory, Encoding, Computer networks, Neural networks
• Public URL: http://researchrepository.napier.ac.uk/Output/2809184