The most fundamental choice an animal has to make when it detects a threat is whether to freeze, reducing its chances of being noticed, or to flee to safety. Here we show that Drosophila melanogaster exposed to looming stimuli in a confined arena either freeze or flee. The probability of freezing versus fleeing is modulated by the fly's walking speed at the time of threat, demonstrating that freeze/flee decisions depend on behavioral state. We describe a pair of descending neurons crucially implicated in freezing. Genetic silencing of DNp09 descending neurons disrupts freezing yet does not prevent fleeing. Optogenetic activation of both DNp09 neurons induces running and freezing in a state-dependent manner. Our findings establish walking speed as a key factor in defensive response choices and reveal a pair of descending neurons as a critical component in the circuitry mediating selection and execution of freezing or fleeing behaviors.

Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

The Q-system is a binary expression system that works well across species. Here we report the development and demonstrate applications of a split-QF system that drives strong expression in , is repressible by QS and inducible by a small non-toxic molecule quinic acid. The split-QF system is fully compatible with existing split-GAL4 and split-LexA lines, thus greatly expanding the range of possible advanced intersectional experiments and anatomical, physiological and behavioural assays in and in other organisms.

Sensory cortices are active in the absence of external sensory stimuli. To understand the nature of this ongoing activity, we used two-photon calcium imaging to record from over 10,000 neurons in the visual cortex of mice awake in darkness while monitoring their behavior videographically. Ongoing population activity was multidimensional, exhibiting at least 100 significant dimensions, some of which were related to the spontaneous behaviors of the mice. The largest single dimension was correlated with the running speed and pupil area, while a 16-dimensional summary of orofacial behaviors could predict ~45% of the explainable neural variance. Electrophysiological recordings with 8 simultaneous Neuropixels probes revealed a similar encoding of high-dimensional orofacial behaviors across multiple forebrain regions. Representation of motor variables continued uninterrupted during visual stimulus presentation, occupying dimensions nearly orthogonal to the stimulus responses. Our results show that a multidimensional representation of motor state is encoded across the forebrain, and is integrated with visual input by neuronal populations in primary visual cortex.

Background

Dendritic spines are tiny protrusions found along the dendrites of neurons, and their number is a measure of the density of synaptic connections. Altered density and morphology is observed in several pathologies, and spine formation as well as morphological changes correlate with learning and memory. The detection of spines in microscopy images and the analysis of their morphology is therefore a prerequisite for many studies. We have developed a new open-source, freely available, plugin for ImageJ/FIJI, called Spot Spine, that allows detection and morphological measurements of spines in three dimensional images.

Method

Local maxima are detected in spine heads, and the intensity distribution around the local maximum is computed to perform the segmentation of each spine head. Spine necks are then traced from the spine head to the dendrite. Several parameters can be set to optimize detection and segmentation, and manual correction gives further control over the result of the process.

Results

The plugin allows the analysis of images of dendrites obtained with various labeling and imaging methods. Quantitative measurements are retrieved including spine head volume and surface, and neck length.

Conclusion

The plugin and instructions for use are available at https://imagej.net/plugins/spot-spine.

CA1 pyramidal neurons from animals that have acquired hippocampal tasks show increased neuronal excitability, as evidenced by a reduction in the postburst afterhyperpolarization (AHP). Studies of AHP plasticity require stable long-term recordings, which are affected by the intracellular solutions potassium methylsulphate (KMeth) or potassium gluconate (KGluc). Here we show immediate and gradual effects of these intracellular solutions on measurement of the AHP and basic membrane properties, and on the induction of AHP plasticity in CA1 pyramidal neurons from rat hippocampal slices. The AHP measured immediately after establishing whole-cell recordings was larger with KMeth than with KGluc. In general, the AHP in KMeth was comparable to the AHP measured in the perforated-patch configuration. However, KMeth induced time-dependent changes in the intrinsic membrane properties of CA1 pyramidal neurons. Specifically, input resistance progressively increased by 70% after 50 min; correspondingly, the current required to trigger an action potential and the fast afterdepolarization following action potentials gradually decreased by about 50%. Conversely, these measures were stable in KGluc. We also demonstrate that activity-dependent plasticity of the AHP occurs with physiologically relevant stimuli in KGluc. AHPs triggered with theta-burst firing every 30 s were progressively reduced, whereas AHPs elicited every 150 s were stable. Blockade of the apamin-sensitive AHP current (I(AHP)) was insufficient to block AHP plasticity, suggesting that plasticity is manifested through changes in the apamin-insensitive slow AHP current (sI(AHP)). These changes were observed in the presence of synaptic blockers, and therefore reflect changes in the intrinsic properties of the neurons. However, no AHP plasticity was observed using KMeth. In summary, these data show that KMeth produces time-dependent changes in basic membrane properties and prevents or obscures activity-dependent reduction of the AHP. In whole-cell recordings using KGluc, repetitive theta-burst firing induced AHP plasticity that mimics learning-related reduction in the AHP.

Single-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of intensity-based glutamate-sensing fluorescent reporter (iGluSnFR) that are functionally brighter; detect submicromolar to millimolar amounts of glutamate; and have blue, cyan, green, or yellow emission profiles. These variants could be imaged in vivo in cases where original iGluSnFR was too dim, resolved glutamate transients in dendritic spines and axonal boutons, and allowed imaging at kilohertz rates.

Fluorescence is magical. Shine one color of light on a fluorophore and it glows in another color. This property allows imaging of biological systems with high sensitivity─we can visualize individual fluorescent molecules in an ocean of nonfluorescent ones.

Fluorescence microscopy has long been used to study isolated cells, both living and dead, but the development of newer, tailored fluorophores is swiftly expanding the use of fluorescence imaging to more complicated systems such as intact animals. In the latest in a long string of transformative work, Sletten and co-workers introduce dyes shrouded with multiple polymer chains─effectively star polymers with a bright fluorophore at the center.

An approaching predator and self-motion toward an object can generate similar looming patterns on the retina, but these situations demand different rapid responses. How central circuits flexibly process visual cues to activate appropriate, fast motor pathways remains unclear. Here we identify two descending neuron (DN) types that control landing and contribute to visuomotor flexibility in Drosophila. For each, silencing impairs visually evoked landing, activation drives landing, and spike rate determines leg extension amplitude. Critically, visual responses of both DNs are severely attenuated during non-flight periods, effectively decoupling visual stimuli from the landing motor pathway when landing is inappropriate. The flight-dependence mechanism differs between DN types. Octopamine exposure mimics flight effects in one, whereas the other probably receives neuronal feedback from flight motor circuits. Thus, this sensorimotor flexibility arises from distinct mechanisms for gating action-specific descending pathways, such that sensory and motor networks are coupled or decoupled according to the behavioral state.

The central nervous system can generate various behaviours, including motor responses, which we can observe through video recordings. Recent advancements in genetics, automated behavioural acquisition at scale, and machine learning enable us to link behaviours to their underlying neural mechanisms causally. Moreover, in some animals, such as the Drosophila larva, this mapping is possible at unprecedented scales of millions of animals and single neurons, allowing us to identify the neural circuits generating particular behaviours.These high-throughput screening efforts are invaluable, linking the activation or suppression of specific neurons to behavioural patterns in millions of animals. This provides a rich dataset to explore how diverse nervous system responses can be to the same stimuli. However, challenges remain in identifying subtle behaviours from these large datasets, including immediate and delayed responses to neural activation or suppression, and understanding these behaviours on a large scale. We introduce several statistically robust methods for analyzing behavioural data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioural spaces aimed at detecting subtle deviations in behaviour. 3) A generative model for larval behavioural sequences, providing a benchmark for identifying complex behavioural changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioural screen focused on responses to an air puff, analyzing data from 280,716 larvae across 568 genetic lines.Author Summary There is a significant gap in understanding between the architecture of neural circuits and the mechanisms of action selection and behaviour generation.Drosophila larvae have emerged as an ideal platform for simultaneously probing behaviour and the underlying neuronal computation [1]. Modern genetic tools allow efficient activation or silencing of individual and small groups of neurons. Combining these techniques with standardized stimuli over thousands of individuals makes it possible to relate neurons to behaviour causally. However, extracting these relationships from massive and noisy recordings requires the development of new statistically robust approaches. We introduce a suite of statistical methods that utilize individual behavioural data and the overarching structure of the behavioural screen to deduce subtle behavioural changes from raw data. Given our study’s extensive number of larvae, addressing and preempting potential challenges in body shape recognition is critical for enhancing behaviour detection. To this end, we have adopted a physics-informed inference model. Our first group of techniques enables robust statistical analysis within a learned continuous behaviour latent space, facilitating the detection of subtle behavioural shifts relative to reference genetic lines. A second array of methods probes for subtle variations in action sequences by comparing them to a bespoke generative model. Together, these strategies have enabled us to construct representations of behavioural patterns specific to a lineage and identify a roster of ”hit” neurons with the potential to influence behaviour subtly.