Consistent individual differences in behavior, known as behavioral individuality, are pervasive across the animal world and have major ecological and evolutionary consequences. Nevertheless, we still have a limited understanding of what drives individuality and how it emerges during ontogeny. Here, we subjected clonal individuals to a ubiquitous yet critical environmental challenge—the threat of predation—to disentangle the developmental mechanisms of individuality. Under such a salient environmental stressor, among-individual differences may collapse or expand depending on whether there is a single or multiple optimal strategies, demonstrating that individuality itself is a developmentally plastic trait. If, however, the environment does not impact among-individual variation, this suggests that individuality is determined before birth. We continuously tracked the behavior of genetically identical fish (Amazon mollies, Poecilia formosa), reared with or without predation stress, from birth through their first month of life. Predation shifted mean-level behaviors, with predator-exposed individuals swimming more slowly and spending more time near their refuges. However, the magnitude of individuality (as evidenced by repeatability) increased similarly over development in both treatments, indicating that individuality crystallizes robustly over time, even under stress and in a vacuum of genetic variation. Predator-reared fish also exhibited greater within-individual variability in refuge use, suggesting increased behavioral flexibility or disrupted developmental canalization in response to stress. Surprisingly, maternal identity, but not maternal behavior, was the strongest predictor of swimming speed, pointing to non-behavioral maternal effects as a key pre-birth source of behavioral variation. Refuge use however was not at all predicted by maternal identity, indicating that major fitness-related behaviors can have entirely different developmental mechanisms. Collectively, we show that individuality persists despite environmental stress and is seeded before birth through non-genetic factors. Even in the face of a shared environmental challenge, the behavioral trajectories of individuals are unique.
more »
« less
Idiosyncratic learning performance in flies
Individuals vary in their innate behaviours, even when they have the same genome and have been reared in the same environment. The extent of individuality in plastic behaviours, like learning, is less well characterized. Also unknown is the extent to which intragenotypic differences in learning generalize: if an individual performs well in one assay, will it perform well in other assays? We investigated this using the fruit fly Drosophila melanogaster , an organism long-used to study the mechanistic basis of learning and memory. We found that isogenic flies, reared in identical laboratory conditions, and subject to classical conditioning that associated odorants with electric shock, exhibit clear individuality in their learning responses. Flies that performed well when an odour was paired with shock tended to perform well when the odour was paired with bitter taste or when other odours were paired with shock. Thus, individuality in learning performance appears to be prominent in isogenic animals reared identically, and individual differences in learning performance generalize across some aversive sensory modalities. Establishing these results in flies opens up the possibility of studying the genetic and neural circuit basis of individual differences in learning in a highly suitable model organism.
more »
« less
- Award ID(s):
- 1764269
- PAR ID:
- 10328937
- Date Published:
- Journal Name:
- Biology Letters
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1744-957X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Learning and memory are critical functions for all animals, giving individuals the ability to respond to changes in their environment. Within populations, individuals vary, however the mechanisms underlying this variation in performance are largely unknown. Thus, it remains to be determined what genetic factors cause an individual to have high learning ability and what factors determine how well an individual will remember what they have learned. To genetically dissect learning and memory performance, we used theDrosophilasynthetic population resource (DSPR), a multiparent mapping resource in the model systemDrosophila melanogaster, consisting of a large set of recombinant inbred lines (RILs) that naturally vary in these and other traits. Fruit flies can be trained in a “heat box” to learn to remain on one side of a chamber (place learning) and can remember this (place memory) over short timescales. Using this paradigm, we measured place learning and memory for ~49 000 individual flies from over 700 DSPR RILs. We identified 16 different loci across the genome that significantly affect place learning and/or memory performance, with 5 of these loci affecting both traits. To identify transcriptomic differences associated with performance, we performed RNA‐Seq on pooled samples of seven high performing and seven low performing RILs for both learning and memory and identified hundreds of genes with differences in expression in the two sets. Integrating our transcriptomic results with the mapping results allowed us to identify nine promising candidate genes, advancing our understanding of the genetic basis underlying natural variation in learning and memory performance.more » « less
-
Many behaviors that we perform everyday, including something as familiar as making a peanut-butter sandwich, consist of a sequence of recognizable acts. These acts may include, for example, holding a knife and opening a jar. Yet often neither the sequence nor the individual acts are always performed in the exact same way. For example, there are many ways to hold a knife and there are many ways to open a jar, meaning neither of these actions could be called “stereotyped”. A lack of stereotypy makes it difficult for a computer to automatically recognize the individual acts in a sequence. This same problem would apply to other common behaviors, such as walking around somewhere you have not visited before. While we easily recognize it when we see it, walking is not a stereotyped behavior. It consists of a series of movements that differ between individuals, and even in the same individual at different times. So how can someone automatically recognize the individual acts in a non-stereotyped behavior like walking? To begin to find out, Tao et al. developed a mathematical model that can recognize the walking behavior of a fruit fly. Existing recordings of fruit flies walking were analyzed using a type of mathematical model called a Hierarchical Hidden Markov Model (often shortened to HHMM). Such models assume that there are hidden states that influence the behaviors we can see. For example, someone’s chances of going skiing (an observable behavior) depend on whether or not it is winter (a hidden state). The HHMM revealed that the seemingly random wanderings of a fly consist of ten types of movement. These include the “meander”, the “stop-and-walk”, as well as right turns and left turns. Exposing the flies to a pleasant odor – in this case, apple cider vinegar – altered how the flies walked by changing the time they spent performing each of the different types of movement. All flies in the dataset used the same ten movements, but in different proportions. This means that each fly showed an individual pattern of movement. In fact, the differences between flies are so great that Tao et al. argue that there is no such thing as an average walk for a fruit fly. The model represents a complete description of how fruit flies walk. It thus provides clues to the processes that transform an animal’s sensory experiences into behavior. But it also has potential clinical applications. Similar models for human behaviors could help reveal behaviors that are abnormal because of disease. Normal behaviors also show variability, and some diseases increase or decrease this variability. By making it easier to detect these changes, mathematical models could support earlier diagnosis of medical conditions.more » « less
-
Despite the substantial success of deep learning for modulation classification, models trained on a specific transmitter configuration and channel model often fail to generalize well to other scenarios with different transmitter configurations, wireless fading channels, or receiver impairments such as clock offset. This paper proposes Contrastive Learning with Self- Reconstruction called CLSR-AMC to learn good representations of signals resilient to channel changes. While contrastive loss focuses on the differences between individual modulations, the reconstruction loss captures representative features of the signal. Additionally, we develop three data augmentation operators to emulate the impact of channel and hardware impairments without exhaustive modeling of different channel profiles. We perform extensive experimentation with commonly used datasets. We show that CLSR-AMC outperforms its counterpart based on contrastive learning for the same amount of labeled data by significant average accuracy gains of 24.29%, 17.01%, and 15.97% in Additive White Gaussian Noise (AWGN), Rayleigh+AWGN, and Rician+AWGN channels, respectively.more » « less
-
null (Ed.)Abstract The sense of smell is an essential modality for many species, in particular nocturnal and crepuscular mammals, to gather information about their environment. Olfactory cues provide information over a large range of distances, allowing behaviours ranging from simple detection and recognition of objects, to tracking trails and navigating using odour plumes from afar. In this review, we discuss the features of the natural olfactory environment and provide a brief overview of how odour information can be sampled and might be represented and processed by the mammalian olfactory system. Finally, we discuss recent behavioural approaches that address how mammals extract spatial information from the environment in three different contexts: odour trail tracking, odour plume tracking and, more general, olfactory-guided navigation. Recent technological developments have seen the spatiotemporal aspect of mammalian olfaction gain significant attention, and we discuss both the promising aspects of rapidly developing paradigms and stimulus control technologies as well as their limitations. We conclude that, while still in its beginnings, research on the odour environment offers an entry point into understanding the mechanisms how mammals extract information about space.more » « less
An official website of the United States government

