Spatiotemporal response properties of optic-flow processing neurons.
A central goal in sensory neuroscience is to fully characterize a neuron's input-output relation. However, strong nonlinearities in the responses of sensory neurons have made it difficult to develop models that generalize to arbitrary stimuli. Typically, the standard linear-nonlinear models break down when neurons exhibit stimulus-dependent modulations of their gain or selectivity. We studied these issues in optic-flow processing neurons in the fly. We found that the neurons' receptive fields are fully described by a time-varying vector field that is space-time separable. Increasing the stimulus strength, however, strongly reduces the neurons' gain and selectivity. To capture these changes in response behavior, we extended the linear-nonlinear model by a biophysically motivated gain and selectivity mechanism. We fit all model parameters directly to the data and show that the model now characterizes the neurons' input-output relation well over the full range of motion stimuli.
Weber
Franz
F
Department of Systems and Computational Neurobiology, Max-Planck-Institute of Neurobiology, 82152 Martinsried, Germany. weberf@neuro.mpg.de
Machens
Christian K
CK
Borst
Alexander
A
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D003712:Dendrites / Q000502:physiology; D004175:Diptera; D016014:Linear Models; D008839:Microelectrodes; D008959:Models, Neurological; D009038:Motion; D009039:Motion Perception / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D010775:Photic Stimulation; D013997:Time Factors