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Serotonin has been implicated in the regulation of a wide range of brain functions involving alternative behavioral states, including the control of mood, aggression, sex, and sleep. Here, we report that in the nematode Caenorhabditis elegans, serotonin controls a switch between two distinct, on/off states of egg-laying behavior. Through quantitative analysis of the temporal pattern of egg-laying events, we determined that egg laying can be modeled as a novel random process, in which animals fluctuate between discrete behavioral states: an active state, during which eggs are laid in clusters, and an inactive state, during which eggs are retained. Single-cell ablation experiments indicate that two pairs of motor neurons, HSNL/HSNR and VC4/VC5, can induce the active phase by releasing serotonin. These neurons also release acetylcholine, which appears to trigger individual egg-laying events within the active phase. Genetic experiments suggest that determination of the behavioral states observed for C. elegans egg laying may be mediated through protein kinase C-dependent (PKC-dependent) modulation of voltage-gated calcium channels.

Since even before the time of Alexander Graham Bell's revolutionary invention, engineers and scientists have studied the phenomenon of speech communication with an eye on creating more efficient and effective systems of human-to-human and human-to-machine communication. Starting in the 1960s, digital signal processing (DSP), assumed a central role in speech studies, and today DSP is the key to realizing the fruits of the knowledge that has been gained through decades of research. Concomitant advances in integrated circuit technology and computer architecture have aligned to create a technological environment with virtually limitless opportunities for innovation in speech communication applications. In this text, we highlight the central role of DSP techniques in modern speech communication research and applications. We present a comprehensive overview of digital speech processing that ranges from the basic nature of the speech signal, through a variety of methods of representing speech in digital form, to applications in voice communication and automatic synthesis and recognition of speech. The breadth of this subject does not allow us to discuss any aspect of speech processing to great depth; hence our goal is to provide a useful introduction to the wide range of important concepts that comprise the field of digital speech processing. A more comprehensive treatment will appear in the forthcoming book, Theory and Application of Digital Speech Processing [101].

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