Statistical discrimination of natural modes of motion in rat exploratory behavior

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Abstract

We analyze the locomotor behavior of the rat during exploration, and show that digitally collected data (time series of positions) provide a sufftcient basis for establishing that the rat uses several distinct modes of motion (first, second, third, and sometimes fourth gear). The distinction between these modes is obtained by first segmenting the time series into sequences of data points occurring between arrests (as ascertained within the resolution of the data acquisition system). The statistical distribution of the maximal amount of motion occurring within each of these episodes is then analyzed and shown to be multi modal. This enables us to decompose motion into distinct modes. In one application of this decomposition we show that the ethological ad hoc notion of stopping behavior corresponds to progression without leaving first gear. We do so by showing that the spatial spread of such progressions is confined to a small 20–50 cm range in a 6.5 m diameter arena. This provides a justification for a construct of ‘staying in place’. This construct is not defined in terms of position in objective space, but purely in terms of the rat's own behavior. We test the generality of our method by applying it to mouse exploratory behavior.

Section snippets

General introduction

This paper consists of three parts. In the first we show that given a time series of the coordinates of an exploring rat, one can decompose the behavior into several distinct modes of motion (first, second, third, and sometimes fourth gear). In the second part we illustrate the usefulness of the method, by showing that what has previously been labeled as stopping in rats, really corresponds to progression without leaving first gear. Finally, we test the generality of our method by analyzing

Density estimators

Density estimators (Silverman, 1980) are smoothed versions of histograms. They use moving bin location to obtain a more precise estimate of the concentration of observations at a given value. In this way the discontinuities displayed in the histogram, which are an artificial result of the non-overlapping bins, can be avoided, and better estimates are obtained. The curves obtained through a density estimator involve a choice of degree of smoothing. We choose the minimal degree of smoothing for

Part I: the decomposition of locomotor behavior into distinct modes

When placed in a large novel arena, rats alternate between full arrests, partial arrests involving scanning and stepping, walking, and running. We do not know a priori whether these categories represent convenient ad hoc landmarks within a continuum, or whether they stand for distinct and perhaps also discrete natural building blocks of behavior.

Part II: application to rat stopping behavior

We can now study the correspondence between behavior patterns that were previously established ad hoc, and the various modes of progression. One such pattern is stopping. It has been previously defined in a study performed in our lab in the following way: ‘When exploring a new environment a rat alternates between progressing (i.e. walking forward or running) and stopping: it progresses forward, then stops by performing so-called closing steps (in which the stepping leg lands besides the

Part 111: the generality of our method

Is our method applicable to any data set of a time series of x, y coordinates sampled at a rate of at least 10 Hz/s? Is it applicable to, e.g. mouse behavior? Can it distinguish modes of motion in this behavior, and if so, how many gears do mice use and what are the velocities that characterize the various modes? Do mice have a lingering mode, and if so, what is the spatial spread of lingering episodes? While a detailed comparison to rats is beyond the scope of the present study, preliminary

Discussion

Detailed studies of rat exploratory behavior done in our lab (Eilam and Golani, 1989, Eilam and Golani, 1990, Eilam and Golani, 1994, Eilam et al., 1989, Golani et al., 1993, Tchernichovski and Golani, 1995, Tchernichovski et al., 1996, Tchernichovski et al., 1998) and by others (Whishaw and Tomie, 1995, Cools et al., 1989, Cools et al., 1997, Gingras and Cools, 1997, Szechtman et al., 1998, Szechtman et al., 1999) revealed that in a novel environment rats establish an intricate structure of

Acknowledgements

Tirza Stern performed the stages of data digitation. We would also like to thank Ofer Tchernichovski for providing us with the hooded rats raw data, Neri Kafkafi for many productive discussions and suggestions, and both of them and Mark Shik for useful comments on the manuscript. This study was supported by a grant from the Adams Super Center for Brain Studies, Tel Aviv University.

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