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Adrian Haith
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| Bayesian models of sensorimotor adaptation Many kinds of disturbances can be responsible for a dip in motor performance. Distinguishing between these and identifying the true cause of an error is important to maintain performance. This problem can be framed as a Bayesian estimation problem - infer which disturbance caused the error given noisy observations and noisy motor execution. I have recently collaborated with Carl Jackson and Chris Miall at the PRISM Lab, University of Birmingham, to test whether human behaviour matches the Bayesian approach to solving this problem. |
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Cerebellar-based motor learning models Various computational models have been proposed of the role of the cerebellum in motor control and adaptation. In particular, some models have proposed a feedforward connectivity in which the cerebellar output contributes directly to the motor command, while Porrill and Dean have recently advocated a recurrent architecture for cerebellar connectivity. There turn out to striking and complementary differences between the computational properties of these two models, with different architectures being best-suited to adaptation different kinds of sensorimotor disturbance. |
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Stochastic integrate-and-fire neural models I have worked with Liam Paninski to develop efficient methods for computating the likelihood of parameters within a stochastic integrate-and-fire neuron model. |