On the capacity of transient internal states in liquid-state machines

Authors:
Karim El-Laithy;Martin Bogdan
Affiliations:
Faculty of Mathematics and Computer Science, Dept. of Computer Engineering, Universität Leipzig, Germany;Faculty of Mathematics and Computer Science, Dept. of Computer Engineering, Universität Leipzig, Germany
Venue:
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Year:
2011

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Abstract

Liquid-state machines (LSM) represent a class of neural networks that are able to introduce multitasking by implicit representation of input information over the entire network components. How exactly the input information can be represented and how the computations are accomplished, stay however unresolved. In order to tackle this issue, we demonstrate how LSM can process different input information as a varying set of transiently stable states of collective activity. This is performed by adopting a relatively complex dynamic synaptic model. Some light is shed on the relevance of the usage of the developed framework to mimic complex cortical functions, e.g. content-addressable memory.