Brain modelFaculty research conducted in conjunction with The University of Sheffield has developed a computer model which charts what happens in the brain when an action leads to a reward. The model could provide insights into the mechanisms behind motor disorders such as Parkinson’s disease and conditions involving abnormal learning, such as addiction. Faculty researcher Dr Mark Humphries explains:

“We wanted to look at how we learn from feedback – particularly how we learn to associate actions to new unexpected outcomes. To do this we created a series of computational models to show how the firing of dopamine neurons caused by receiving reward ultimately translates into selecting the causative action more frequently in the future.”

Research had already shown that actions are represented in the brain’s outer layer of neural tissue (the cortex) and that rewards activate neurons that release dopamine. The dopamine signals are then sent to the striatum, which plays an important role in how we select which action to take. Together, this evidence suggested that dopamine signals change the strength of connections between cortical and striatal neurons, determining which action is appropriate in a specific circumstance. Until now, though, no model had tested these strands together. Dr Humphries explains why they created the model:

“Essentially, within this area of research, we are tackling a puzzle in which we have an unknown number of pieces and no picture to guide us. Some pieces have been studied individually, so the questions were: could we put the pieces of the puzzle together and prove that they made a coherent picture? And could we guess at the missing pieces? The only way was through using a computational model, which allows us to do things impossible in experiments – provide solutions and guesses for the missing pieces. The fact that the pieces of our puzzle all fitted together to produce a single coherent picture is evidence that we (as a field) are converging on a complete theory for how the brain learns from reward.”

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