Alexay A. Kozhevnikov

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Songs of many songbird species consist of variable sequences of a finite number of syllables. A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables. This is equivalent to the Markov model, in which each syllable is associated with one state, and the transition(More)
Here we present a novel approach to quickly and reliably find long (200 ms - 2 s) stereotyped sequences of sounds ("motifs") in acoustic recordings of birdsong. Robust and time-efficient identification of such sequences is a crucial first step in many studies ranging from development to neuronal basis of motor behavior. Accurately identifying motifs is(More)
The use of wireless neural stimulation devices offers significant advantages for neural stimulation experiments in behaving animals. We demonstrate a simple, low-cost and extremely lightweight wireless neural stimulation device which is made from off-the-shelf components. The device has low power consumption and does not require a high-power RF(More)
Studies of behavioral and neural responses to distorted auditory feedback (DAF) can help shed light on the neural mechanisms of animal vocalizations. We describe an apparatus for generating real-time acoustic feedback. The system can very rapidly detect acoustic features in a song and output acoustic signals if the detected features match the desired(More)
Variable motor sequences of animals are often structured and can be described by probabilistic transition rules between action elements. Examples include the songs of many songbird species such as the Bengalese finch, which consist of stereotypical syllables sequenced according to probabilistic rules (song syntax). The neural mechanisms behind such rules(More)
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