Measuring behavior across scales

Abstract

The need for high-throughput, precise, and meaningful methods for measuring behavior has been amplified by our recent successes in measuring and manipulating neural circuitry. The largest challenges associated with moving in this direction, however, are not technical but are instead conceptual: what numbers should one put on the movements an animal is performing (or not performing)? In this review, I will describe how theoretical and data analytical ideas are interfacing with recently-developed computational and experimental methodologies to answer these questions across a variety of contexts, length scales, and time scales. I will attempt to highlight commonalities between approaches and areas where further advances are necessary to place behavior on the same quantitative footing as other scientific fields.

Deep learning based behavioral analysis enables high precision rodent tracking and is capable of outperforming commercial solutions

“…Accurate analysis of rodent behavior is of the utmost importance when assessing treatment efficacy in preclinical research. The rapid development of new tools and molecular interventions in rodents, as well as the ever increasing number of available transgenic mouse lines, increase the need to accurately and efficiently detect and quantify rodent behavior [1][2][3] . Typically, behavioral analysis relies on commercial equipment to track an animal's path of movement or measure the time spent in specific areas of testing arenas.…”

A hybrid versatile method for state estimation and feature extraction from the trajectory of animal behavior

“…Recent technological development of compact and inexpensive cameras and/or global positioning system (GPS) devices has facilitated convenient monitoring and recording of animal behavior (Brown and de Bivort, 2018;Dell et al, 2014;Egnor and Branson, 2016). However, the behavioral data generated through these approaches are frequently represented as a few simple measures, such as velocity, migratory distance, or the probability of reaching a particular goal, due to the challenges related to identification of specific aspects of behavior to be analyzed; in other words, it is still difficult to figure out how we can describe an animal behavior meaningfully (Berman, 2018). Owing to poor description of behavior, dynamic neural activity, for example, is not sufficiently interpreted even though simultaneous optical monitoring can measure a large number of time-series neural activities (Alivisatos et al, 2012;Landhuis, 2017).…”

RAD-Behavior (Recombining Atomized, Discretized, Behavior): A new framework for the quantitative analysis of behavioral execution

“…Though several high-throughput behavioral classification techniques have recently been developed 1-7 , questions remain about how to leverage the resulting datasets generated from such techniques [8][9][10] . In this paper, we describe an 45…”

“…Quantitative comparisons of behaviors are challenging for many reasons 10 . Key among these challenges is the difficulty in scoring sets of behaviors that may share few obvious similarities.…”

“…These are longstanding challenges in the scientific study of behavior that have been addressed in myriad and creative ways by scientists from many disciplines, frequently in ways unique to specific research aims and questions. Yet these challenges remain largely unsolved at broad scales and novel conceptual approaches are required to leverage the rapidly developing technological capabilities available to behaviorists 10 . The simplicity of RAD-behavior (breaking down complex behaviors to their core 'atoms' (Box 1), measuring and discretizing these subunits, and recombining 105 these character states into a single string) coupled with its integrability with new technologies (e.g.…”