Parallel processing is ubiquitous in the nervous systems and is a critical component of its computational power. Neural signals throughout the nervous system diverge into parallel excitatory and inhibitory pathways that later converge onto downstream neurons. Understanding how these converging channels of information shape the output of the downstream target neurons is an important component of understanding how neural systems process information and direct behavior. Neural computations in parallel networks must be able to process signal information from sensory stimuli and be robust to variability, or “noise”, introduced within the network. This dissertation examines how a neuron's spike patterns and its ability encode stimulus information are shaped by the signal and noise properties in the excitatory and inhibitory synaptic input they receive. These issues are investigated in retinal ganglion cells, which encode all visual information the brain receives from the eyes. I hope that the data and analyses presented here help progress our understanding of how the nervous system processes information and directs behavior.
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