Signal Transformation in the Early Visual System

University Of California-San Francisco, San Francisco CA

Investigators

Abstract

One of the basic requirements for understanding how the brain works is to know what the electrical activity of each neuron "means" with respect to an animal's behavior. The electrical signals are manifested as temporal sequences of very brief electrical impulses, commonly called spikes, which are the currency of the nervous system. Neurons respond to stimuli by spiking, and they communicate with each other by spiking. Interpreting what such spiking patterns encode has been of longstanding scientific interest because it translates into knowing what each neuron does within a circuit. The aim of this project is to go one step further. The goal is to examine how the spike pattern received by one neuron is transformed into a new, output spike pattern. Although much has been learned about what spike sequences encode, the coding transformation which occurs from neuron to neuron has rarely been quantitatively investigated. Only when this recoding of spike patterns is understood can a neuron?s functional role be considered fully characterized. The research naturally progresses in three stages. First, spike trains will be recorded from connected pairs of neurons responding to naturalistic stimuli. The investigators have chosen to record from neurons in the primate lateral geniculate nucleus because their input, provided from retinal ganglion cells, and their output can be accessed readily. Second, they will apply a novel analytical method to determine the optimal stimulus features encoded by each cell's spike train. This involves a computationally intensive search through the stimulus space to arrive at the stimulus representations which carry the maximum amount of information. By comparing the optimal representations for the input and output spike trains, the feature transformation from one neuron to the next will be revealed. Third, they will test the predictive power of their method by using the found optimal stimuli and systematically degraded stimuli to probe neural responses in a second series of recording experiments. Such a test will serve to validate the computational basis of the feature transformation. Having chosen a problem of very fundamental interest, the methods being developed will be valuable for studying signal communication and representation in any system of connected neurons. The outcome will provide a basis for comprehending the processing capabilities of neurons with multiple inputs, which are common throughout the brain, and could be applied to engineered systems designed for interpreting visual scenes.