Effects of Noise on the Electrosensory System of Mormyrid Electric Fish

Oregon Health & Science University, Portland OR

Investigators

Abstract

The brain handles a continuous temporal flow of sensory information, but there also is noise in the physiological signal that affects the uncertainty in information processing. At the microscopic level of the synapses, which are the functional contacts between nerve cells (neurons), the relevant signals are the small variable electrophysiological events called synaptic potentials. When learning occurs, there is a change in the relative weighted value of these synaptic potentials, for how strongly they influence the targeted neurons to fire and ultimately to drive a particular behavior. At the more macroscopic level of a functional region of the brain, there are systems properties of the response to large numbers of cellular inputs. The system needs to optimize the trade-off between accuracy, which may take some time to establish, and adaptability, which often requires rapid adjustment to changes in the sensory environment. This collaborative project combines a neural network approach with computational biology and experimental neurophysiology, to test a statistical model on rules of adaptive learning. Experiments involve a species of weakly electric fish, which produces a pulsed electrical field around itself. Objects in the water distort the field, and the lateral-line sensors on the body detect these distortions. Neurons in the electrosensory lateral-line lobe (ELL) in their brain receive signals from the lateral line sensors, and also receive signals temporally locked to the motor command for the electric pulses. Responses of cells in the ELL adapt to changing electrosensory conditions, and this adaptability allows these neurons to 'store' an image of the fish's expectation of the electrosensory field. It is not clear whether the adaptive learning seen at the cellular level can explain collective neural activity in the brain of the behaving fish. This project involves a novel aspect of modeling emphasizing statistical properties of the synaptic system in the presence of noise, and making testable predictions about how noise can affect the accuracy of the fish's stored expectation of the signal. Results of this project will clarify how synaptic learning rates and signal processing interact, and how the effects of noise are handled by the system. The impact will extend beyond electrosensory processing and computational neuroscience to studies of learning and of how cerebellar-like brain structures function, and there is highly cross-disciplinary training involved.