Redundant Input Cancellation by a Bursting Neural Network

• Main Author: Bol, Kieran G.
• Other Authors: Longtin, Andre
• Language: en
• Published: Université d'Ottawa / University of Ottawa 2011
• Subjects: neural computation; neural learning; plasticity; adaptive filter; noise cancellation; LIF; neural modeling; neural network; burst learning; neuroscience; neurophysics; weakly electric fish; neural circuits; sensory processing; feedback; spike-timing dependent plasticity; burst induced depression; leaky integrate-and-fire model; stochastic modeling
• Online Access: http://hdl.handle.net/10393/20061; http://dx.doi.org/10.20381/ruor-4650

One of the most powerful and important applications that the brain accomplishes is solving the sensory "cocktail party problem:" to adaptively suppress extraneous signals in an environment. Theoretical studies suggest that the solution to the problem involves an adaptive filter, which learns to remove the redundant noise. However, neural learning is also in its infancy and there are still many questions about the stability and application of synaptic learning rules for neural computation. In this thesis, the implementation of an adaptive filter in the brain of a weakly electric fish, A. Leptorhynchus, was studied. It was found to require a cerebellar architecture that could supply independent frequency channels of delayed feedback and multiple burst learning rules that could shape this feedback. This unifies two ideas about the function of the cerebellum that were previously separate: the cerebellum as an adaptive filter and as a generator of precise temporal inputs.