Information coding in neurons: using systems-identification to link cellular, circuit and behavioral neurophysiology

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OBJECTIVE: The long-term objective of our laboratory's research program is to link the activity of neurons to our higher-level perceptual experiences. To understand how neuronal activity is linked to our perception of the external world, we need to know the mathematical rules neurons use to encode information in their action potential (or spike) output. This is usually referred to as understanding the “neural code”. A major unanswered question is how do the nonlinearities in single neurons and correlations between many neurons shape the neural code?******APPROACH: We propose to study the neural code at three levels. The first level of investigation addresses the nonlinearities of single neurons at the cellular level using intracellular recordings. The second level addresses how the intact neural circuit of the retina influences the nonlinearities of single retinal ganglion cells. For the third level, we will use extracellular electrophysiological recordings from the visual cortex to link correlations in neuronal activity to visually-guided behaviors.******The goal of this research grant is to link these three separate levels of investigation (cellular, circuit and systems) using quantitative systems-identification approaches. To do this, we will study neural coding at each level using similar experimental design, stimuli and analysis methods. The benefit of using a systems-identification approach is that it allows us to describe the neural code using a common set of functional tools such as linear filters and static nonlinearities. By having a common description of the the cellular, circuit and behavioral neurophysiology, we are more likely to find a common computational framework******SPECIFIC AIMS: The overall goal is to create functional models of our cellular, circuit and systems neurophysiological data. The data for this proposal will be collected in collaboration with our partners and in our lab. We have a large library of both intra- and extracellular neuronal recordings and behaviors to work with. The challenge has been to find the right quantitative models to reveal the neural code at each level.******Aim 1: Determine the cellular-level rules and nonlinearities that dendrites use to integrate multiple inputs.***Aim 2: Determine the circuit-level rules and nonlinearities that retinal ganglion cells use to integrate visual and optogenetically-delivered inputs. ***Aim 3: Determine the behavioral-level rules that the visual cortex uses to integrate correlated visual inputs.******Specifically, we will focus on the role of nonlinearities and neural correlations.******IMPACT: Using functional models to link cellular, circuit and behavioral neurophysiology will provide us with a mathematically-based understanding of how neurons process information, which is critical for our larger goal of revealing how neural circuits in the brain underlie perception and behavior.