Title :
Modeling structural plasticity in the barn owl auditory localization system with a spike-time dependent Hebbian learning rule
Author :
Mysore, Shreesh P. ; Quartz, Steven R.
Author_Institution :
Control & Dynamical Syst. Program, California Inst. of Technol., Pasadena, CA, USA
fDate :
31 July-4 Aug. 2005
Abstract :
Auditory localization behavior in barn owls is mediated by the integration of topographically encoded visual and auditory space maps. In juvenile owls, disruption of the audio visual map alignment by exposure to spectacles that laterally shift the visual input results in behavioral adaptation over the course of several weeks. It has been reported in literature that this adaptation is produced by architectural plasticity in the neural circuits encoding the space maps. It is known that this plasticity is guided by visual input in a topographic manner, and that the error signal is embedded in the firing dynamics of neurons in the inferior colliculus. In this work, we use leaky integrate-and-fire neurons to model the key elements in the auditory localization circuit of barn owls. We demonstrate that a Hebbian spike time dependent learning rule, coupled with an activity-dependent mechanism that promotes growth, can account for the essentials of circuit level plasticity associated with prism experience. We point out the importance of inhibition in both the normal functioning of this circuit, and prism induced plasticity, and comment on potential mechanisms for activity induced growth.
Keywords :
Hebbian learning; hearing; neural nets; neurophysiology; Hebbian learning rule; activity induced growth; activity-dependent mechanism; auditory localization circuit; auditory localization system; barn owl; circuit level plasticity; integrate-and-fire neurons; prism induced plasticity; spike time dependent learning rule; structural plasticity modeling; Biological system modeling; Biology computing; Circuits; Electronic mail; Hebbian theory; Neurons; OWL; Optical sensors; Predictive models; Space technology;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556363