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Classical time-evolving spectral analysis techniques utilize a sliding window approach that fails to exploit overarching spectrotemporal structures that are known to occur in many real-world signals. In particular, many biological signals have the distinct quality of having few defining spectral characteristics. We propose an algorithm for efficiently(More)
A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse ar X iv :1 70 9. 09 72 3v 1 [ st at .M E ] 2 7 Se p 20 17 neural activity over time or(More)
We present a compartmentalized approach to finding the maximum a-posteriori (MAP) estimate of a latent time series that obeys a dynamic stochastic model and is observed through noisy measurements. We specifically consider modern signal processing problems with non-Markov signal dynamics (e.g. group sparsity) and/or non-Gaussian measurement models (e.g.(More)
A problem of classification of local field potentials (LFPs), recorded from the prefrontal cortex of a macaque monkey, is considered. An adult macaque monkey is trained to perform a memory based saccade. The objective is to decode the eye movement goals from the LFP collected during a memory period. The LFP classification problem is modeled as that of(More)
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