1,721,037 research outputs found
Bengalese finch - train and test data
No full textContains recordings and manual annotations of the song from 4 male Bengalese finches.
Original data source for the recordings and the annotations: https://figshare.com/articles/dataset/Bengalese_Finch_song_repository/4805749
Original reference:
Nicholson D, Queen JE, J. Sober S. 2017.
Bengalese finch song repository.
doi:10.6084/m9.figshare.4805749.v5<br
Marmoset - train and test data
No full textContains recordings and manual annotations of calls from pairs of male and female marmosets.
Manual annotations were created by the original authors and manually corrected for training and testing DAS.
Original data source for the recordings and the annotations: https://osf.io/q4bm3/
Original reference:
Landman R, Sharma J, Hyman JB, Fanucci-Kiss A, Meisner O, Parmar S, Feng G, Desimone R. 2020.
Close-range vocal interaction in the common marmoset (Callithrix jacchus).
PLOS ONE 15:e0227392. doi:10.1371/journal.pone.0227392<br
Neural computation in small sensory systems
No full textDas Ziel von computational neuroscience ist, neuronale Transformationen zu beschreiben und deren Mechanismen und Funktionen zu beleuchten. Diese Doktorarbeit kombiniert Experiment, Datenanalyse und Modelle um neuronale Kodierung anhand des auditorischen Systems von Feldheuschrecke und Grille zu erforschen. Der erste Teil befasst sich mit der neuronalen Repräsentation von Balzsignalen in Feldheuschrecken. In Rezeptoren ist die Kodierung dieser Signale homogen - alle Neuronen bilden den Reiz gleich ab. In nachgeschalteten Zellen wird die Kodierung spärlicher, sowohl auf Ebene der Zeit als auch der Zellpopulation. Es entsteht ein labeled line code, bei dem unterschiedliche Nervenzellen unterschiedliche Merkmale des Stimulus abbilden. Dieser Transformation liegt eine nichtlineare Kombination von mehreren Stimulusmerkmalen zu Grunde. Die erhöhte Spezifizität von Neuronen dritter Ordnung ermöglicht eine einfache Art der Musterklassifikation, bei der die Zeitpunkte bestimmter Reizelemente innerhalb des Signals ignoriert werden können. Die beschriebene Reiztransformation repräsentiert einen Mechanismus für die Erkennung zeitlich redundanter Kommunikationssignale, wie sie von vielen Insekten produziert werden. Im zweiten Teil wird gezeigt, dass die spektrale und zeitliche Abstimmung von Neuronen zweiter Ordnung bei Grillen von der Komplexität des Reizes abhängt. Während die Abstimmung für Reize mit nur einer Trägerfrequenz breit ist, führen Reize mit mehreren Trägerfrequenzen zu einer Schärfung. Hierdurch kann Information über einzelne Komponenten eines komplexen Signals in der Kodierung erhalten werden. Ein statisches Netzwerkmodell zeigt, dass diese adaptive Abstimmung mit Mechanismen erzeugt werden kann, die in Nervensystemen vieler Organismen vorkommen. Wie diese Doktorabeit zeigt, vereinen Insekten einfach aufgebaute und gut zugängliche Nervensysteme mit komplexen Reiztransformationen. Dies macht sie zu produktiven Modellorganismen für die Neurowissenschaften
Mouse - train and test data
No full textContains recordings and manual annotations of ultrasonic vocalizations (USVs) of female and male residents towards an intruder mouse.
Manual annotations were created for training and testing DAS.
Original data source for the recordings:https://data.donders.ru.nl/collections/di/dcn/DSC_620840_0003_891?0
Original reference:
Ivanenko A, Watkins P, Gerven MAJ van, Hammerschmidt K, Englitz B. 2020.
Classifying sex and strain from mouse ultrasonic vocalizations using deep learning.
PLOS Computational Biology 16:e1007918.
USVs from a female resident towards a female intruder used for training and testing DAS:
Rfem_Afem01_annotations.csv, Rfem_Afem01.npz
Rfem_Afem02_annotations.csv, Rfem_Afem02.npz
USVs from a male resident towards a female intruder used for testing generalization of the female-trained DAS model:
Rmale_Afem01_annotations.csv, Rmale_Afem01.npz
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Drosophila melanogaster - train and test data (multi channel)
No full textContains recordings (on 9 microphone channels) and manual annotations of the courtship song (pulse and sine) of male Drosophila melanogaster.
Manual annotations were created for training and testing DAS.
The recordings were previously unpublished and were first used in:
Clemens J, Coen P, Roemschied FA, Pereira TD, Mazumder D, Aldarondo DE, Pacheco DA, Murthy M. 2018.
Discovery of a New Song Mode in Drosophila Reveals Hidden Structure in the Sensory and Neural Drivers of Behavior.
Current biology 28:2400–2412.e6.<br
Zebra finch - train and test data
No full textContains recordings and manual annotations of directed song (6 syllable types) from a male Zebra finch.
Manual annotations were created for training and testing DAS.
Original data source for the recordings: https://research.repository.duke.edu/concern/datasets/9k41zf38gv (file "blu285_DIR.zip")
Original reference:
Goffinet J, Brudner S, Mooney R, Pearson J. 2021.
Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.
eLife 10:e67855. doi:10.7554/eLife.67855<br
Annotations for audio and video data
No full textContain song annotations for audio and pose tracking data for video dat
Feature extraction and combination underlying decision making during courtship in grasshoppers
No full textTraditionally, perceptual decision making is studied in trained animals and carefully controlled tasks. Here, we sought to elucidate the stimulus features and their combination underlying a naturalistic behavior--female decision making during acoustic courtship in grasshoppers. Using behavioral data, we developed a model in which stimulus features were extracted by physiologically plausible models of sensory neurons from the time-varying stimulus. This sensory evidence was integrated over the stimulus duration and combined to predict the behavior. We show that decisions were determined by the interaction of an excitatory and a suppressive stimulus feature. The observed increase of behavioral response with stimulus intensity was the result of an increase of the excitatory feature's gain that was not controlled by an equivalent increase of the suppressive feature. Differences in how these two features were combined could explain interindividual variability. In addition, the mapping between the two stimulus features and different parameters of the song led us to re-evaluate the cues underlying acoustic communication. Our framework provided a rich and plausible explanation of behavior in terms of two stimulus cues that were extracted by models of sensory neurons and combined through excitatory-inhibitory interactions. We thus were able to link single neuron's feature selectivity and network computations with decision making in a natural task. This data-driven approach has the potential to advance our understanding of decision making in other systems and can inform the search for the neural correlates of behavior
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