Neuroscience makes use of many metaphors in its attempt to explain the relationship between our brain and our behaviour. In this thesis I contrast the most commonly used metaphor - that of computation driven by neuron action potentials - with an alternative view which seeks to understand the brain in terms of an agent learning from the reward signalled by neuromodulators. To explore this reinforcement learning model I construct computational models to assess one of its key claims — that the neurotransmitter dopamine signals unexpected reward, and that this signal is used by the brain to learn control of our movements and drive goal-directed behaviour. In this thesis I develop a selection of computational models that are motivated by either theoretical concepts or experimental data relating to the effects of dopamine. The first model implements a published dopamine-modulated spike timing-dependent plasticity mechanism but is unable to correctly solve the distal reward problem. I analyse why this model fails and suggest solutions. The second model, more closely linked to the empirical data attempts to investigate the relative contributions of firing rate and synaptic conductances to synaptic plasticity. I use experimental data to estimate how model neurons will be affected by dopamine modulation, and use the resulting computational model to predict the effect of dopamine on synaptic plasticity. The results suggest that dopamine modulation of synaptic conductances is more significant than modulation of excitability. The third model demonstrates how simple assumptions about the anatomy of the basal ganglia, and the electrophysiological effects of dopamine modulation can lead to reinforcement learning like behaviour. The model makes the novel prediction that working memory is an emergent feature of a reinforcement learning process. In the course of producing these models I find that both theoretically and empirically based models suffer from methodological problems that make it difficult to adequately support such fundamental claims as the reinforcement learning hypothesis. The conclusion that I draw from the modelling work is that it is neither possible, nor desirable to falsify the theoretical models used in neuroscience. Instead I argue that models and metaphors can be valued by how useful they are, independently of their truth. As a result I suggest that we ought to encourage a plurality of models and metaphors in neuroscience. In Chapter 7 I attempt to put this into practice by reviewing the other transmitter systems that modulate dopamine release, and use this as a basis for exploring the context of dopamine modulation and reward-driven behaviour. I draw on evidence to suggest that dopamine modulation can be seen as part of an extended stress response, and that the function of dopamine is to encourage the individual to engage in behaviours that take it away from homeostasis. I also propose that the function of dopamine can be interpreted in terms of behaviourally defining self and non-self, much in the same way as inflammation and antibody responses are said to do in immunology.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:563742 |
| Date | January 2012 |
| Creators | Kyle, Robert |
| Contributors | Willshaw, David. : Durstewitz, Daniel |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/6263 |
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