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The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog.

Girdhar K, Gruebele M, Chemla YR - PLoS ONE (2015)

Bottom Line: Clustering analysis reveals three known behavioral patterns-scoots, turns, rests-but shows that these do not represent discrete states, but rather extremes of a continuum.The behavioral space not only classifies fish by their behavior but also distinguishes fish by age.With the insight into fish behavior from postural space and behavioral space, we construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.

View Article: PubMed Central - PubMed

Affiliation: Center for Biophysics and Computational Biology, University of Illinois, Urbana, IL, 61801, United States of America.

ABSTRACT
How simple is the underlying control mechanism for the complex locomotion of vertebrates? We explore this question for the swimming behavior of zebrafish larvae. A parameter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish "eigenshapes" as coordinates, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative "postural space" that captures >96% of the observed zebrafish locomotion. Viewed in postural space, swim bouts are manifested as trajectories consisting of cycles of shapes repeated in succession. To classify behavioral patterns quantitatively and to understand behavioral variations among an ensemble of fish, we construct a "behavioral space" using multi-dimensional scaling (MDS). This method turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analysis reveals three known behavioral patterns-scoots, turns, rests-but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. With the insight into fish behavior from postural space and behavioral space, we construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.

No MeSH data available.


Representation of free swimming zebrafish in low-dimensional postural space.(A) Still images of a representative turning bout during free swimming. As discussed in the text, it is convenient to divide the bout into a “turn” region (t = 50–100 ms) followed by a “scoot” region (100–250 ms). (B) Plot of the amplitudes U1(t), U2(t), and U3(t) of the three collective eigenshapes corresponding to the movie in A. Each amplitude undergoes multiple oscillation cycles before returning to zero. The regions marked by dashed lines and labeled as cycles (1–4) in U1(t), U2(t), and U3(t) are obtained from the oscillation cycles in U1(t). The colored dots mark time points corresponding to the still images in A. (C) Representation of a turn bout in postural space. The three-dimensional coordinates of the trajectory are the amplitudes U1(t), U2(t), and U3(t) in B. In this space, the bout involves a turn region (t = 50–100 ms), represented as a bent ellipse (cycle 1), followed by a scoot region (t = 100–250 ms) represented as multiple cycles (2–4) along the flat ellipses, and a final return to the rest behavior. Throughout, time (0–250 ms) is represented by the black—magenta colormap. An analysis of a representative scooting bout is shown in S5 Fig.
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pone.0128668.g003: Representation of free swimming zebrafish in low-dimensional postural space.(A) Still images of a representative turning bout during free swimming. As discussed in the text, it is convenient to divide the bout into a “turn” region (t = 50–100 ms) followed by a “scoot” region (100–250 ms). (B) Plot of the amplitudes U1(t), U2(t), and U3(t) of the three collective eigenshapes corresponding to the movie in A. Each amplitude undergoes multiple oscillation cycles before returning to zero. The regions marked by dashed lines and labeled as cycles (1–4) in U1(t), U2(t), and U3(t) are obtained from the oscillation cycles in U1(t). The colored dots mark time points corresponding to the still images in A. (C) Representation of a turn bout in postural space. The three-dimensional coordinates of the trajectory are the amplitudes U1(t), U2(t), and U3(t) in B. In this space, the bout involves a turn region (t = 50–100 ms), represented as a bent ellipse (cycle 1), followed by a scoot region (t = 100–250 ms) represented as multiple cycles (2–4) along the flat ellipses, and a final return to the rest behavior. Throughout, time (0–250 ms) is represented by the black—magenta colormap. An analysis of a representative scooting bout is shown in S5 Fig.

Mentions: Based on this analysis method, any zebrafish free swimming bout can be represented as a trajectory in the low-dimensional postural space spanned by the three collective eigenshapes described above. Fig 3 shows an example of a zebrafish turning bout and how it can be visualized as a trajectory in the postural space (S5 Fig shows a corresponding scooting bout). At each time point ti of the movie, the zebrafish backbone shape is represented by a set of three amplitudes {Uk(ti)} with k = 1, 2 and 3. Fig 3B plots these three amplitudes Uk vs. time, and in Fig 3C the three amplitudes define the coordinates of this fish’s trajectory in the three-dimensional postural space. Represented in this manner, a swimming bout appears as a sequence of cycles of shapes. For this particular example the swim bout has four cycles as shown by dashed lines in Fig 3B that explore different regions of the postural space in Fig 3C.


The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog.

Girdhar K, Gruebele M, Chemla YR - PLoS ONE (2015)

Representation of free swimming zebrafish in low-dimensional postural space.(A) Still images of a representative turning bout during free swimming. As discussed in the text, it is convenient to divide the bout into a “turn” region (t = 50–100 ms) followed by a “scoot” region (100–250 ms). (B) Plot of the amplitudes U1(t), U2(t), and U3(t) of the three collective eigenshapes corresponding to the movie in A. Each amplitude undergoes multiple oscillation cycles before returning to zero. The regions marked by dashed lines and labeled as cycles (1–4) in U1(t), U2(t), and U3(t) are obtained from the oscillation cycles in U1(t). The colored dots mark time points corresponding to the still images in A. (C) Representation of a turn bout in postural space. The three-dimensional coordinates of the trajectory are the amplitudes U1(t), U2(t), and U3(t) in B. In this space, the bout involves a turn region (t = 50–100 ms), represented as a bent ellipse (cycle 1), followed by a scoot region (t = 100–250 ms) represented as multiple cycles (2–4) along the flat ellipses, and a final return to the rest behavior. Throughout, time (0–250 ms) is represented by the black—magenta colormap. An analysis of a representative scooting bout is shown in S5 Fig.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4489106&req=5

pone.0128668.g003: Representation of free swimming zebrafish in low-dimensional postural space.(A) Still images of a representative turning bout during free swimming. As discussed in the text, it is convenient to divide the bout into a “turn” region (t = 50–100 ms) followed by a “scoot” region (100–250 ms). (B) Plot of the amplitudes U1(t), U2(t), and U3(t) of the three collective eigenshapes corresponding to the movie in A. Each amplitude undergoes multiple oscillation cycles before returning to zero. The regions marked by dashed lines and labeled as cycles (1–4) in U1(t), U2(t), and U3(t) are obtained from the oscillation cycles in U1(t). The colored dots mark time points corresponding to the still images in A. (C) Representation of a turn bout in postural space. The three-dimensional coordinates of the trajectory are the amplitudes U1(t), U2(t), and U3(t) in B. In this space, the bout involves a turn region (t = 50–100 ms), represented as a bent ellipse (cycle 1), followed by a scoot region (t = 100–250 ms) represented as multiple cycles (2–4) along the flat ellipses, and a final return to the rest behavior. Throughout, time (0–250 ms) is represented by the black—magenta colormap. An analysis of a representative scooting bout is shown in S5 Fig.
Mentions: Based on this analysis method, any zebrafish free swimming bout can be represented as a trajectory in the low-dimensional postural space spanned by the three collective eigenshapes described above. Fig 3 shows an example of a zebrafish turning bout and how it can be visualized as a trajectory in the postural space (S5 Fig shows a corresponding scooting bout). At each time point ti of the movie, the zebrafish backbone shape is represented by a set of three amplitudes {Uk(ti)} with k = 1, 2 and 3. Fig 3B plots these three amplitudes Uk vs. time, and in Fig 3C the three amplitudes define the coordinates of this fish’s trajectory in the three-dimensional postural space. Represented in this manner, a swimming bout appears as a sequence of cycles of shapes. For this particular example the swim bout has four cycles as shown by dashed lines in Fig 3B that explore different regions of the postural space in Fig 3C.

Bottom Line: Clustering analysis reveals three known behavioral patterns-scoots, turns, rests-but shows that these do not represent discrete states, but rather extremes of a continuum.The behavioral space not only classifies fish by their behavior but also distinguishes fish by age.With the insight into fish behavior from postural space and behavioral space, we construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.

View Article: PubMed Central - PubMed

Affiliation: Center for Biophysics and Computational Biology, University of Illinois, Urbana, IL, 61801, United States of America.

ABSTRACT
How simple is the underlying control mechanism for the complex locomotion of vertebrates? We explore this question for the swimming behavior of zebrafish larvae. A parameter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish "eigenshapes" as coordinates, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative "postural space" that captures >96% of the observed zebrafish locomotion. Viewed in postural space, swim bouts are manifested as trajectories consisting of cycles of shapes repeated in succession. To classify behavioral patterns quantitatively and to understand behavioral variations among an ensemble of fish, we construct a "behavioral space" using multi-dimensional scaling (MDS). This method turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analysis reveals three known behavioral patterns-scoots, turns, rests-but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. With the insight into fish behavior from postural space and behavioral space, we construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.

No MeSH data available.