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The identification of the timing of the discharges of groups of muscle fibers (motor units) is of utmost importance in research into the strategies employed by the central nervous system in producing muscle force and in the diagnosis of neuromuscular diseases. The process involves the recognition of unique shapes (action potentials) contributed by different motor units at random times throughout a muscle contraction. This paper addresses a specific aspect of the identification process: the decomposition of the compound signal when the action potentials of two or more motor units are superimposed. We propose a cross-time-frequency-based procedure to identify which two (out of a previously identified collection of waveforms) are included in a superposition.

The dorsal cochlear nucleus (DCN) is part of the first stage of auditory processing in the central nervous system. Experimental evidence has provided a conceptual model for a portion of the DCN neural circuit that serves as a basis for the computational model described in this paper. The model consists of four neural populations arranged tonotopically. The pattern of convergence from one population to another and the strengths of those connections are important model parameters. Lumped parameter electrical circuit models are used to model the membrane potential of individual cells. Synapses are simulated by activating variable conductances in postsynaptic cells according to spike activity in presynaptic cells. The level of detail incorporated in the model is a good compromise between biophysical accuracy and the computational tractability required to simulate relatively large networks. Results are shown demonstrating the ability of the model to replicate features of DCN cross-correlation functions and to simulate DCN response properties with quantitative accuracy. The model is a useful tool for exploring hypotheses regarding DCN structure and function.