Global ETD Search

1	Redundant Input Cancellation by a Bursting Neural Network Bol, Kieran G. 20 June 2011 (has links) 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. 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
2	Redundant Input Cancellation by a Bursting Neural Network Bol, Kieran G. 20 June 2011 (has links) 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. 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
3	Redundant Input Cancellation by a Bursting Neural Network Bol, Kieran G. 20 June 2011 (has links) 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. 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
4	Redundant Input Cancellation by a Bursting Neural Network Bol, Kieran G. January 2011 (has links) 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. 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

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