02938nas a2200229   4500008004100000245013700041210006900178520212800247653001802375653001802393653001402411653001302425653001602438653003502454653001502489653002402504653003602528653002702564100002302591700002702614856006702641       2019                        eng d00aDissimilar interaction between dopaminergic and cholinergic systems in the initiation of emission of 50-kHz and 22-kHz vocalizations0 aDissimilar interaction between dopaminergic and cholinergic syst3 a<p>Rats emit 22-kHz or 50-kHz ultrasonic vocalizations (USVs) to signal their emotional state to other conspecifics. The 22-kHz USVs signal a negative emotional state while 50-kHz USVs reflect a positive affective state. The initiation of 22-kHz USVs is dependent on the activity of cholinergic neurons within the laterodorsal tegmental nucleus that release acetylcholine along the medial cholinoceptive vocalization strip. Emission of 50-kHz USVs is dependent upon the activation of dopaminergic neurons located within the ventral tegmental area that release dopamine into the medial shell of the nucleus accumbens. There have been reports that showed an antagonistic interaction between acetylcholine and dopamine during the expression of emotional states, and dopamine agonists decreased carbachol-induced emission of 22-kHz USVs. The current study tests the hypothesis that initial antagonism of dopamine receptors by systemic haloperidol or intraacumbens raclopride should increase the subsequent emission of 22 kHz USVs induced by carbachol from the lateral septum. Our findings showed that antagonism of dopaminergic signaling either via systemic haloperidol or via intracerebral raclopride did not alter the number of emitted 22-kHz USVs. Thus, inhibition of the mesolimbic dopamine system did not increase the magnitude of a negative emotional state. It was found, however, that prolonged emission of 22-kHz USVs initiated by carbachol caused a delayed rebound emission (R) of 50-kHz USVs appearing after 300 s of emission of 22-kHz USVs, i.e., when the response was subsiding. The R-50-kHz USVs were predominantly frequency modulated (FM) USVs and their number was directly proportional to the number of recorded 22-kHz USVs. The emission of R-50-kHz USVs was reversed by systemic pretreatment with haloperidol or intraacumbens injection of raclopride. It is argued that the R-50-kHz USVs represent a rebound emotional state that is opposite in valence and arousal induced by carbachol. Importantly, prolonged emission of amphetamine-induced 50 kHz USVs failed to show any vocalization rebound effect.</p>
10aAcetylcholine10aAntipsychotic10aCarbachol10aDopamine10aHaloperidol10aLaterodorsal tegmental nucleus10aRaclopride10aRebound 50-kHz USVs10aUltrasonic vocalizations (USVs)10aVentral tegmental area1 aSilkstone, Michael1 aBrudzynski, Stefan, M.  uhttps://linkinghub.elsevier.com/retrieve/pii/S009130571930259X01975nas a2200205   4500008004100000245009300041210006900134520129400203653001801497653003101515653003601546100002101582700001801603700001601621700002101637700002901658700002001687700002101707856004101728       2019                        eng d00aApplying convolutional neural networks to the analysis of mouse ultrasonic vocalizations0 aApplying convolutional neural networks to the analysis of mouse 3 a<p>Many species in diverse taxonomic groups, including rodents, bats, and insects, communicate with complex ultrasonic vocalizations (USVs) (&gt;20 kHz). Two main components of processing and analyzing USV recordings include detection and classification of syllable types. Recently we developed an efficient algorithm for detecting mouse USVs (Automatic Mouse Ultrasound Detector (A-MUD)). The main challenge is detecting USVs under conditions with a low signal-to-noise ratio, while minimizing rates of false positives (FP). Mice produce many short USVs (&lt; 10 millisecond) that inflate FPs. We aimed to improve the detection of mouse USVs with A-MUD by classifying vocalizations into three discrete syllable types (with 0, 1, or &ge;2 frequency-jumps) or FP. Supervised Convolutional Neural Networks (CNNs) were fed by 2D Gammatone Spectrograms (GSs) adapted to the frequency range of mice. Evaluation of performance shows that CNNs yielded an overall accuracy of 95&plusmn;1.2% and macro-F1 score of 90&plusmn;2.7%. In contrast, multilayer feed-forward neural networks fed by vectorized spectrograms provided an overall accuracy of only 85.4&plusmn;1.9% and macro-F1 score of 75.4&plusmn;2.9%, and therefore, the chosen CNNs outperformed this conventional classification method.</p>
10aDeep learning10aImage-based classification10aUltrasonic vocalizations (USVs)1 aAbbasi, Reyhaneh1 aBalazs, Peter1 aNOLL, Anton1 aNICOLAKIS, Doris1 aMarconi, Maria, Adelaide1 aZala, Sarah, M.1 aPenn, Dustin, J.  uhttps://bio.acousti.ca/nn/node/57419