Natural behavior is the language of the brain

Abstract

The breadth and complexity of natural behaviors inspires awe. Understanding how our perceptions, actions, and internal thoughts arise from evolved circuits in the brain has motivated neuroscientists for generations. Researchers have traditionally approached this question by focusing on stereotyped behaviors, either natural or trained, in a limited number of model species. This approach has allowed for the isolation and systematic study of specific brain operations, which has greatly advanced our understanding of the circuits involved. At the same time, the emphasis on experimental reductionism has left most aspects of the natural behaviors that have shaped the evolution of the brain largely unexplored. However, emerging technologies and analytical tools make it possible to comprehensively link natural behaviors to neural activity across a broad range of ethological contexts and timescales, heralding new modes of neuroscience focused on natural behaviors. Here we describe a three-part roadmap that aims to leverage the wealth of behaviors in their naturally occurring distributions, linking their variance with that of underlying neural processes to understand how the brain is able to successfully navigate the everyday challenges of animals' social and ecological landscapes. To achieve this aim, experimenters must harness one challenge faced by all neurobiological systems, namely variability, in order to gain new insights into the language of the brain.

Figure 1.. Animals have remarkably diverse behavioral repertoires.

The stereotyped conditioned and specialized behaviors that are typically studied in systems neuroscience and neuroethology are a small part of the range of behaviors they produce, many of which are shared across species (social interaction, predator avoidance, prey capture, and so on) but for which we know very little about the underlying neural mechanisms. For example, with the exception of spatial representations in the medial temporal lobe first pioneered by O’Keefe^–, relatively few data are available that detail the effects of navigation and exploration on perceptual and cognitive functions^– despite the fact that the ability to move through space has both been a foundational pressure on brain evolution and routinely accounts for large portions of daily activity in all animals. Similarly, the neuroscience of the song learning behavior in oscines has been studied extensively generating unique insights into sequence motor learning but has taught us very little about brain mechanisms involved in natural vocal exchanges. Spatial exploration and natural communication behaviors, however, are highly variable, making them difficult to study in conventional frameworks.

Figure 2.. Context-dependent variability of behavior, demonstrated by co-articulation of speech.

The formant frequency representation for single words (‘other’) can change dramatically in different sentences (A,B) and be distinct when spoken in isolation (C). This context dependence, and the contrast between natural context and repeated trials in isolation, is also mirrored in the sensory domain. For example, in vision an edge at a given orientation can appear in many different visual contexts, and such stimuli within natural scenes evoke different responses than the standard repeated presentation of a similar stimulus in isolation^,.

Figure 3.. Behavioral and neural signatures of variability

. (A) Zebrafinches sing to conspecifics of the opposite sex (directed song) as well as in isolation (non-directed song). While only small acoustic differences are evident between these contexts, the underlying neural activity shows striking variability. Adapted with permission from Hessler and Doupe. (B) Head-fixed mice on a spherical treadmill spontaneously alternate between a stationary state and locomotion. Responses to identical visual stimuli result in approximately two-times greater response when the mouse is running. Without quantification of behavioral state, this would appear as unexplained variance^,. Adapted with permission from Niell and Stryker. (C) In recordings of monkeys performing a visual task over several hours, task performance and arousal (as measured by pupil diameter) vary slowly. This is accompanied by a slow drift in the first principal component of neural activity, evident at the population level. Adapted with permission from Cowley et al..

Figure 4.. Pathways from natural behaviors to neural computations.

Marmoset prey capture comprises several visual processes that have heretofore been studied in isolation, rather than as different components of a single integrative, visuo-motor behavior. Employing complementary machine vision technologies to annotate the behavior of the animal and high-density neural recordings provides is a powerful strategy to identify behavioral components in different contexts through quantitative ethograms, while employing analytical approaches that reduce the dimensionality and identify critical covariance between brain and behavior. This approach is made possible by modern technologies for quantifying behaviors at time scales that mirror brain signals, longitudinal imaging and neural recordings and powerful modeling tools.