Selfee: Self-supervised Features Extraction of animal behaviors

Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in the laboratories for behavior analysis. However, it has not been achieved to use a fully unsupervised method to extract comprehensive and discriminative features directly from raw behavior video frames for annotation and analysis purposes. Here, we report a self supervised feature extraction (Selfee) convolutional neural network with multiple downstream applications to process video frames of animal behavior in an end to end way. Visualization and classification of the extracted features (Meta representations) validate that Selfee processes animal behaviors in a comparable way of human understanding. We demonstrate that Meta representations can be efficiently used to detect anomalous behaviors that are indiscernible to human observation and hint in depth analysis. Furthermore, time series analyses of Meta representations reveal the temporal dynamics of animal behaviors. In conclusion, we present a self supervised learning approach to extract comprehensive and discriminative features directly from raw video recordings of animal behaviors and demonstrate its potential usage for various downstream applications.

Sleep Need, the Key Regulator of Sleep Homeostasis, Is Indicated and Controlled by Phosphorylation of Threonine 221 in Salt Inducible Kinase 3

Sleep need drives sleep and plays a key role in homeostatic regulation of sleep. So far sleep need can only be inferred by animal behaviors and indicated by electroencephalography (EEG). Here we report that threonine 221 (T221) of the salt inducible kinase 3 (SIK3) was important for the catalytic activity and stability of SIK3. T221 phosphorylation in the mouse brain indicates sleep need: more sleep resulting in less phosphorylation and less sleep more phosphorylation during daily sleep/wake cycle and after sleep deprivation (SD). Sleep need was reduced in SIK3 loss of function (LOF) mutants and by T221 mutation to alanine (T221A). Sleep rebound after SD was also decreased in SIK3 LOF and T221A mutant mice. Other kinases such as SIK1 and SIK2 or other sites in SIK3 do not fulfil criteria to be both an indicator and a controller of sleep need. Our results reveal SIK3 T221 phosphorylation as the first and only chemical modification which indicates and controls sleep need.

Drosophila tachykininergic neurons modulate the activity of two groups of receptor-expressing neurons to regulate aggressive tone

Neuropeptides influence animal behaviors through complex molecular and cellular mechanisms, many of which are difficult to predict solely from synaptic connectivity. Here, we uncovered two separate downstream targets that are differentially modulated by the neuropeptide tachykinin, which promotes Drosophila aggression. Tachykinin from a single sexually dimorphic group of neurons recruits two separate downstream groups of neurons. One downstream group, synaptically connected to the tachykinergic neurons, expresses the receptor TkR86C and is necessary for aggression. Tachykinin supports the strength of cholinergic excitatory synaptic transmission between the tachykinergic and TkR86C downstream neurons. The other downstream group expresses the TkR99D receptor and is recruited primarily when tachykinin is over-expressed in the source neurons. This circuit reconfiguration correlates with the quantitative and qualitative enhancement of aggression observed when tachykinin is present in excess. Our data highlight how the amount of neuropeptide released from a small number of neurons can reshape the activity patterns of multiple downstream neural populations.

A fluorescent sensor for the real-time measurement of extracellular oxytocin dynamics in the brain

Oxytocin (OT), a hypothalamic neuropeptide that acts as a neuromodulator in the brain, orchestrates a variety of animal behaviors. However, the relationship between brain OT dynamics and complex animal behaviors remains largely elusive, partly because of the lack of a suitable technique for its real-time recording in vivo. Here, we describe MTRIAOT, a G protein-coupled receptor-based green fluorescent OT sensor with a large dynamic range, optimal affinity, ligand specificity to OT orthologs, minimal effects on downstream signaling, and long-term fluorescence stability. By combining viral gene delivery and fiber photometry-mediated fluorescence measurements, we demonstrated the utility of MTRIAOT for real-time detection of brain OT dynamics in living mice. Importantly, MTRIAOT-mediated measurements revealed "OT oscillation," a hitherto unknown rhythmic change in OT levels in the brain. MTRIAOT will allow the analysis of OT dynamics in a wide variety of physiological and pathological processes.

Animal Farm—a complex artificial life 3D framework

The development of computer-generated ecosystem simulations are becoming more common due to the greater computational capabilities of machines. Because natural ecosystems are very complex, simplifications are required for implementation. This simulation environment o er a global view of the system and generate a lot of data to process and analyse, which are difficult or impossible to do in nature. 3D simulations, besides of the scientific advantages in experiments, can be used for presentation, educational and entertainment purposes too. In our simulated framework (Animal Farm) we have implemented a few basic animal behaviors and mechanics to observe in 3D. All animals are controlled by an individual logic model, which determines the next action of the animal, based on their needs and surrounding environment.

Long-range sequential dependencies precede complex syntactic production in language acquisition

To convey meaning, human language relies on hierarchically organized, long-range relationships spanning words, phrases, sentences, and discourse. The strength of the relationships between sequentially ordered elements of language (e.g., phonemes, characters, words) decays following a power law as a function of sequential distance. To understand the origins of these relationships, we examined long-range statistical structure in the speech of human children at multiple developmental time points, along with non-linguistic behaviors in humans and phylogenetically distant species. Here we show that adult-like power-law statistical dependencies precede the production of hierarchically-organized linguistic structures, and thus cannot be driven solely by these structures. Moreover, we show that similar long-range relationships occur in diverse non-linguistic behaviors across species. We propose that the hierarchical organization of human language evolved to exploit pre-existing long-range structure present in much larger classes of non-linguistic behavior, and that the cognitive capacity to model long-range hierarchical relationships preceded language evolution. We call this the Statistical Scaffolding Hypothesis for language evolution.1Significance StatementHuman language is uniquely characterized by semantically meaningful hierarchical organization, conveying information over long timescales. At the same time, many non-linguistic human and animal behaviors are also often characterized by richly hierarchical organization. Here, we compare the long-timescale statistical dependencies present in language to those present in non-linguistic human and animal behaviors as well as language production throughout childhood. We find adult-like, long-timescale relationships early in language development, before syntax or complex semantics emerge, and we find similar relationships in non-linguistic behaviors like cooking and even housefly movement. These parallels demonstrate that long-range statistical dependencies are not unique to language and suggest a possible evolutionary substrate for the long-range hierarchical structure present in human language.