Self-control with spiking and non-spiking neural networks playing games

Journal of Physiology, Paris
Chris ChristodoulouAristodemos Cleanthous

Abstract

Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sigh...Continue Reading

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Citations

Jul 18, 2015·Bio Systems·Azadeh Hassannejad Nazir, Hans Liljenström
Mar 23, 2011·IEEE Transactions on Neural Networks·Vassilis VassiliadesChris Christodoulou
Aug 16, 2011·Brain Research·Aristodemos Cleanthous, Chris Christodoulou
Dec 18, 2010·Neural Computation·Chris Christodoulou, Aristodemos Cleanthous

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