Detailed quantification of neural dynamics across the entire brain will be the key to genuinely understanding perception and behavior. With the recent developments in microscopy and biosensor engineering, the zebrafish has made a grand entrance in neuroscience as its small size and optical transparency enable imaging access to its entire brain at cellular and even subcellular resolution. However, until recently many neurobiological insights were largely correlational or provided little mechanistic insight into the brain-wide population dynamics generated by diverse types of neurons. Now with increasingly sophisticated behavioral, imaging, and causal intervention paradigms, zebrafish are revealing how entire vertebrate brains function. Here we review recent research that fulfills promises made by the early wave of technical advances. These studies reveal new features of brain-wide neural processing and the importance of integrative investigation and computational modelling. Moreover, we outline the future tools necessary for solving broader brain-scale circuit problems.

Behavior determines how an animal interacts with its environment. For an ecologist, this interaction, and its consequences for the survival of the animal, may be the primary focus of their research. A neuroscientist, by contrast, may be interested in behavior as the ultimate function of the brain. This has led to different approaches to studying behavior. An ecologist may choose to study diverse species adapted to particular environments and focus on the richness of natural behavior. Neuroscientists often prefer to take a reductionist approach, focusing on isolated components of behavior in artificial conditions that allow tight control of experimental parameters. By permitting repeatability of conditions across experiments and across labs, this makes possible comparisons between experiments and provides statistical power to relate behavior to neural function. The downside of this approach is that the brain evolved to generate behavior in complex natural environments, which may not be captured under reduced conditions. Ideally, we would want an experimental system where we can study naturalistic behaviors combined with tight control of experimental parameters and comprehensive measurements of behavioral output and physiology.

Behavior tracking

Analyzing behavior presents several technical challenges, both to capture biological movement in all its complexity and to convert it into simple numerical measures to allow for quantitative analysis. This is further complicated when we wish to combine natural behavior with physiological recordings or neural circuit manipulations.

Categorization of Swimming Patterns in Larvae: Complex behaviors can often be described in terms of sequences of more basic motor patterns (Tinbergen 1951). This can allow a simplified description of behavior and provide an indication of how the underlying neural systems are organized. However, the question of how to divide behavior into discrete units in time is a difficult one that is often settled by subjective analysis based on experienced observation, although, more recently, researchers have made efforts to segment behavior in an unbiased way via unsupervised machine learning methods.

Tracking individuals in groups: It is often necessary to track multiple fish together. Tracking systems developed for single individuals can be used when it is not necessary to follow individual paths, for example, to measure the mean distance between animals. Some systems are more adapted to group settings and recently developed machine learning algorithms can maintain correct identities for indefinite time periods. These algorithms extract an abstract fingerprint in the image for each animal and use it to identify them automatically even after they cross or disappear from view. Automatic analysis of interaction behavior in zebrafish can now be done in naturalistic conditions.

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