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Sequence processing and learning with a reservoir computing approach

Janßen, L.M. (2020) Sequence processing and learning with a reservoir computing approach.

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Abstract:Sequence learning is an inherent human ability, which plays a critical part in human intelligence. The brain is capable of rapidly processing and learning sequential information. Since so many aspects of human cognition deal with sequence learning, the question was raised how neural sequence learning and processing can be simulated. Research suggests that the use of chunks, defined as a memory representation of a basic sequence of items, can make sequential learning more efficient. This shows that the learning of chunks plays a vital role in sequential behaviour and learning. The focus of this study is to simulate how these basic sequences can be learned with a neural network, based on the assumption that such a sequence is just an association between items. In particular, this project aims to understand how such a basic sequence can be learned in a sequential manner, which means presenting each item of a sequence one by one and learn during that process. With the use of reservoir computing a network was built. In the standard approach of reservoir computing, the sequential nature of the network lies within the reservoir, given by random, sparse, and fixed connections between the nodes in the reservoir. In this way, the reservoir offers a set of fixed and randomly organized sequences, which can be used for sequence learning. Learning then occurs in the connections between the reservoir and the output nodes. However, the initial simulation results casted doubts on the cognitive ability of the reservoir computing approach to address human sequential learning because it failed to learn a basic sequence in a sequential manner. To solve this issue a new approach is introduced, in which the role of learning in reservoir computing is changed. By learning sequences within the reservoir instead of from the reservoir to the output nodes a basic sequence could be learned in a sequential manner. The network simulated the learning and reactivation of five sequences with twenty items within the reservoir. In this way, a first step was made towards a better approach for modelling human sequential learning.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:77 psychology
Programme:Psychology MSc (66604)
Link to this item:http://purl.utwente.nl/essays/85448
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