Mapping Sub-Second Structure in Mouse Behavior

Wiltschko, Alexander B.; Johnson, Matthew; Iurilli, Giuliano; Peterson, Ralph E.; Katon, Jesse; Pashkovski, Stan L.; Abraira, Victoria E.; Adams, Ryan P.; Datta, Sandeep Robert

doi:10.1016/j.neuron.2015.11.031

Cited by 633 publications

(805 citation statements)

References 41 publications

Supporting

Mentioning

765

Contrasting

Order By: Relevance

“…For example Wiltschko et al (2015) fit a hidden Markov model directly to the data instead of inferring it after clustering as we have done. Thus, their optimization is simultaneously on the Markovian transitions between states as well as the shape of the states themselves.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Systematic exploration of unsupervised methods for mapping behavior

Todd

Kain²,

Bivort

2017

Phys. Biol.

View full text Add to dashboard Cite

To fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their infancy, and there have been few systematic considerations of all the methodological options. We compared the performance of seven distinct mapping methods in clustering a wavelettransformed data set consisting of the x-and y-positions of the six legs of individual flies. Legs were automatically tracked by small pieces of fluorescent dye, while the fly was tethered and walking on an air-suspended ball. We find that there is considerable variation in the performance of these mapping methods, and that better performance is attained when clustering is done in higher dimensional spaces (which are otherwise less preferable because they are hard to visualize). High dimensionality means that some algorithms, including the non-parametric watershed cluster assignment algorithm, cannot be used. We developed an alternative watershed algorithm which can be used in high-dimensional spaces when a probability density estimate can be computed directly. With these tools in hand, we examined the behavioral space of fly leg postural dynamics and locomotion. We find a striking division of behavior into modes involving the fore legs and modes involving the hind legs, with few direct transitions between them. By computing behavioral clusters using the data from all flies simultaneously, we show that this division appears to be common to all flies. We also identify individual-to-individual differences in behavior and behavioral transitions. Lastly, we suggest a computational pipeline that can achieve satisfactory levels of performance without the taxing computational demands of a systematic combinatorial approach.

show abstract

Section: Discussionmentioning

confidence: 99%

“…(2) This data then undergoes pre-processing and may be dimensionally expanded using time frequency analysis to capture postural dynamics (e.g. Berman et al 2014 andWiltschko et al 2015). (3) Dimensionality reduction is employed to facilitate subsequent computational steps.…”

Section: Introductionmentioning

confidence: 99%

Systematic exploration of unsupervised methods for mapping behavior

Todd

Kain²,

Bivort

2017

Phys. Biol.

View full text Add to dashboard Cite

show abstract

“…behavioral states). While the standard GLM framework can still accommodate these cases (Pillow et al 2008;Fründ et al 2014), more complex types of non-stationarities affecting the stimulus response mapping can be modelled by combining LNs with hidden Markov models (HMM), in which the HMM models the switching between states and one LN model per state captures state-specific input-output transformations ( (Escola et al 2011), see (Wiltschko et al 2015) for a related approach).…”

Section: Extensions Of the Standard Linear-nonlinear Modelmentioning

confidence: 99%

“…Computational models applied to such data will facilitate both interpreting population neural activity and connecting neural activity with behavior. In parallel, the use of unsupervised classification methods has revealed stereotyped structure in animal behavior -an animal's movements over time can be described as sequences of discrete behavioral modules (Vogelstein et al 2014;Berman et al 2014;Wiltschko et al 2015). Computational modeling's task will now be to determine how sensory cues and internal states affect behavioral sequencing, and how neural codes underlie the choice of behavioral modules.…”

Section: Prospectsmentioning

confidence: 99%

The use of computational modeling to link sensory processing with behavior in Drosophila

Clemens

Murthy

2017

Preprint

View full text Add to dashboard Cite

A major goal of systems neuroscience is to understand how the brain represents information, and how those representations are used to drive behavior. Even though genetic model organisms like Drosophila grant unprecedented experimental access to the nervous system for manipulating and recording neural activity, the complexity of natural stimuli and natural behaviors still poses a challenge for solving the connections between neural activity and behavior. Here, we advocate for the use of computational modeling to complement (and enhance) the Drosophila toolkit. We first lay out a modelling framework for making sense of the relation between natural sensory stimuli, neuronal responses, and natural behavior. We then highlight how this framework can be used to reveal how neural circuits drive behavior, using selected case studies.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2720v1 | CC BY 4.0 Open Access |

show abstract

“…Here, we may search for inspiration from recent machine-learning-based classification of mouse behaviour, indicating that movements are combinations of different elementary movements, subsecond postures of mouse body language which form 'syllables', like those in language [64].…”

Section: Current Pitfallsmentioning

confidence: 99%

Attending to and neglecting people: bridging neuroscience, psychology and sociology

Hari

Sams

Nummenmaa

2016

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

One contribution of 15 to a theme issue 'Attending to and neglecting people'. Human behaviour is context-dependent-based on predictions and influenced by the environment and other people. We live in a dynamic world where both the social stimuli and their context are constantly changing. Similar dynamic, natural stimuli should, in the future, be increasingly used to study social brain functions, with parallel development of appropriate signal-analysis methods. Understanding dynamic neural processes also requires accurate time-sensitive characterization of the behaviour. To go beyond the traditional stimulus-response approaches, brain activity should be recorded simultaneously from two interacting subjects to reveal why human social interaction is critically different from just reacting to each other. This theme issue on Attending to and neglecting people contains original work and review papers on person perception and social interaction. The articles cover research from neuroscience, psychology, robotics, animal interaction research and microsociology. Some of the papers are co-authored by scientists who presented their own, independent views in the recent Attention and Performance XXVI conference but were brave enough to join forces with a colleague having a different background and views. In the future, information needs to converge across disciplines to provide us a more holistic view of human behaviour, its interactive nature, as well as the temporal dynamics of our social world.

show abstract

Mapping Sub-Second Structure in Mouse Behavior

Cited by 633 publications

References 41 publications

Systematic exploration of unsupervised methods for mapping behavior

Systematic exploration of unsupervised methods for mapping behavior

The use of computational modeling to link sensory processing with behavior in Drosophila

Attending to and neglecting people: bridging neuroscience, psychology and sociology

Contact Info

Product

Resources

About