A new running theme on the blog: cool uses of behavioral quantification.
One of the most exciting directions in behavioral science are the advances in behavioral quantification. Science often advances by being able to perform ever more precise measurements from ever-increasing amounts of data. Thanks to the increasing power of computers and advances in machine learning, we are now able to automatically extract massive amounts of behavioral data at a level of detail that was previously unobtainable.
A great example of this is a recently published paper out of Janelia Farm. Using an absolutely shocking 400,000 flies, the authors systematically activated small subsets of neurons and then observed what behaviors they performed. First, can you imagine a human scoring every moment of four hundred thousand animals as they behaved over fifteen minutes? That is 12.1 billion frames of data to sort through and classify.
Kristan Branson – the corresponding author on the paper – has been developing two pieces of software that allows for efficient and fast estimation of behavior. The first, Ctrax, tracks individual animals as they move around a small arena and assigns a position, an orientation, and various postural features (for instance, since they are fruit flies we can extract the angle of each wing). The second, JAABA, then uses combinations of these features, such as velocity, interfly distance, and so on, in order to identify behaviors. Users annotate videos with examples of when an animal is performing a particular behavior, and then the program will generate examples in other videos that it believes are the same behavior. An iterative back-and-forth between user and machine gradually narrows down what counts as a particular behavior and what doesn’t, eventually allowing fully-automated classification of behavior in new videos.
Then once you have this pipeline you can just stick a bunch of animals into a plate under a camera, activate said neural populations, let them do whatever they feel like doing, and get gobs and gobs of data. This allows you to understand at neural precision which neurons are responsible for any arbitrary behavior you desire. This lets you build maps – maps that help you understand where information is flowing through the brain. And since you know which of these lines are producing which behaviors, you can then go and find even more specific subsets of neurons that let you identify precise neural pathways.
Here are two examples. Flies sometimes walk backwards (moonwalking!). If you look at the image below, you can see (on the bottom) all the different neurons labeled in a fly brain that had an effect on this backward locomotion, and in the upper-right the more specific areas where the neurons are most likely located. In fruit fly brains, the bulbous protrusions where these colors are found are the eyes of the animal, with a couple flecks in the central brain.
This turns out to be incredibly accurate. Some of this moonwalking circuit was recently dissected and a set of neurons from the eye into the brain was linked to causing this behavior. The neurons (in green below) are in exactly the place you’d expect from the map above! They link to a set of neurons known as the ‘moonwalker descending neurons’ which sends signals to the nerve (spinal) cord that cause the animal to walk backwards.
Of course, sometimes it can be more complicated. When a male fly is courting a female fly, he will extend one wing and vibrate it to produce a song. Here are the neurons related to that behavior (there are a lot):
There are two key points from this quantification. First, the sheer amount and quality of data it is possible to gain access to and score these days is allowing us to have immense statistical precision on when and in which contexts behaviors are occurring. Second, the capacity to find new things is increasing because we can be increasingly agnostic to what we are looking for (so it is easier to find surprises in the data!).
References
Mapping the Neural Substrates of Behavior. Robie et al 2017.
See also: Big behavioral data: psychology, ethology and the foundations of neuroscience. Gomez-Marin et al 2014.
neuroecology 