In the nervous system, sensory information is represented in single neurons by sequences of action potentials—brief, isolated pulses having identical waveforms—occurring randomly in time. These signals are usually modeled as point processes; however, these point processes have a dependence structure and, because of the presence of a stimulus, are non-stationary. Thus, sophisticated non-Gaussian signal processing techniques are needed to analyze data recorded from sensory neurons to determine what aspects of the stimulus are being emphasized and how emphatic that representation might be. A paper analyzes well-established data analysis techniques for single-neuron discharge patterns. Another recent paper describes how we applied our theory of information processing to neural coding. Another paper describes information theoretic (capacity) results for neural populations and hints at how they can be applied to neural prosthetics.