A Neural Circuit Arbitrates between Persistence and Withdrawal in Hungry Drosophila.

In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, we show that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. We further show that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, our data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive

Adaptation of Drosophila larva foraging in response to changes in food resources.

Peer reviewed: TrueFunder: Max-Planck-Gesellschaft; FundRef: http://dx.doi.org/10.13039/501100004189Funder: Alexander von Humboldt-Stiftung; FundRef: http://dx.doi.org/10.13039/100005156All animals face the challenge of finding nutritious resources in a changing environment. To maximize lifetime fitness, the exploratory behavior has to be flexible, but which behavioral elements adapt and what triggers those changes remain elusive. Using experiments and modeling, we characterized extensively how Drosophila larvae foraging adapts to different food quality and distribution and how the foraging genetic background influences this adaptation. Our work shows that different food properties modulated specific motor programs. Food quality controls the traveled distance by modulating crawling speed and frequency of pauses and turns. Food distribution, and in particular the food-no food interface, controls turning behavior, stimulating turns toward the food when reaching the patch border and increasing the proportion of time spent within patches of food. Finally, the polymorphism in the foraging gene (rover-sitter) of the larvae adjusts the magnitude of the behavioral response to different food conditions. This study defines several levels of control of foraging and provides the basis for the systematic identification of the neuronal circuits and mechanisms controlling each behavioral response

Adaptation of spontaneous activity in the developing visual cortex

Spontaneous activity drives the establishment of appropriate connectivity in different circuits during brain development. In the mouse primary visual cortex, two distinct patterns of spontaneous activity occur before vision onset: local low-synchronicity events originating in the retina and global high-synchronicity events originating in the cortex. We sought to determine the contribution of these activity patterns to jointly organize network connectivity through different activity-dependent plasticity rules. We postulated that local events shape cortical input selectivity and topography, while global events homeostatically regulate connection strength. However, to generate robust selectivity, we found that global events should adapt their amplitude to the history of preceding cortical activation. We confirmed this prediction by analyzing in vivo spontaneous cortical activity. The predicted adaptation leads to the sparsification of spontaneous activity on a slower timescale during development, demonstrating the remarkable capacity of the developing sensory cortex to acquire sensitivity to visual inputs after eye-opening

The evolutionary origins of Lévy walk foraging.

We study through a reaction-diffusion algorithm the influence of landscape diversity on the efficiency of search dynamics. Remarkably, the identical optimal search strategy arises in a wide variety of environments, provided the target density is sparse and the searcher's information is restricted to its close vicinity. Our results strongly impact the current debate on the emergentist vs. evolutionary origins of animal foraging. The inherent character of the optimal solution (i.e., independent on the landscape for the broad scenarios assumed here) suggests an interpretation favoring the evolutionary view, as originally implied by the Lévy flight foraging hypothesis. The latter states that, under conditions of scarcity of information and sparse resources, some organisms must have evolved to exploit optimal strategies characterized by heavy-tailed truncated power-law distributions of move lengths. These results strongly suggest that Lévy strategies-and hence the selection pressure for the relevant adaptations-are robust with respect to large changes in habitat. In contrast, the usual emergentist explanation seems not able to explain how very similar Lévy walks can emerge from all the distinct non-Lévy foraging strategies that are needed for the observed large variety of specific environments. We also report that deviations from Lévy can take place in plentiful ecosystems, where locomotion truncation is very frequent due to high encounter rates. So, in this case normal diffusion strategies-performing as effectively as the optimal one-can naturally emerge from Lévy. Our results constitute the strongest theoretical evidence to date supporting the evolutionary origins of experimentally observed Lévy walks

Construction of a fractal patch environment.

<p>Illustration of a search landscape with <i>N</i>_<i>p</i> = 3 patches and <i>N</i>_<i>t</i> = 15000 targets (5000 targets per patch), forming Lévy dust distributions (see main text). Here, <i>β</i> = 2.5, <i>d</i>₀ = 2, <i>d</i>_max = <i>M</i>/10 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005774#pcbi.1005774.e001" target="_blank">Eq (1)</a>, and <i>γ</i> = 2.0, <i>r</i>₀ = 500, <i>r</i>_max = <i>M</i> = 10⁴ in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005774#pcbi.1005774.e002" target="_blank">Eq (2)</a>. The parameters are chosen so that the patches do not overlap. Dotted lines are only a guide to visually delimit the patches regions.</p

Output distribution of step lengths for a <i>μ</i> = 2.0 Lévy searcher in a <i>super-dense</i> landscape.

<p>In the simulations, <i>l</i>_<i>t</i> = 2.5 with <i>N</i>_<i>t</i> = 50000 targets homogeneously placed. The distribution takes into account the first 10⁴ search steps, including non-truncated moves that end up without detecting a target and also a relatively large number of truncated steps due to targets encounters. Numerical simulation data are represented by circles. Dashed and dotted lines are, respectively, best fits to Brownian-like exponential and truncated power-law pdfs. The inset details the large-steps regime. Statistical data inference (MLE and AIC methods) indicates that the output distribution of step lengths in the super-dense regime is not properly described by a superdiffusive power-law (Lévy-like) pdf. Instead, it shows the signature of a Brownian motion.</p

Heterogeneous search landscapes with representative trajectories of different strategies.

<p>Fragmented search landscapes containing <i>N</i>_<i>t</i> = 10⁴ targets placed in <i>N</i>_<i>p</i> = 10 heterogeneous patches (gray regions) with: (A) same average distance between inner targets, , and radii uniformly distributed in the range 0.03<i>M</i> ≤ <i>R</i>^(<i>p</i>) ≤ 0.3<i>M</i>, <i>M</i> = 10⁴; (B) same radius, <i>R</i>^(<i>p</i>) = 0.1<i>M</i>, and uniformly distributed in the range ; and (C) distinct sizes uniformly distributed in the range 0.03<i>M</i> ≤ <i>R</i>^(<i>p</i>) ≤ 0.3<i>M</i>, but fixed number (10³) of inner targets per patch, so that . The darker the patch, the higher its homogeneous density of inner targets. We also show typical paths of a searcher with power-law (Lévy-like) distributions of step lengths displaying different degrees of diffusivity: nearly ballistic (<i>μ</i> = 1.1), superdiffusive (<i>μ</i> = 2.0), and Brownian (<i>μ</i> = 3.0). In this illustrative example the search ends upon the finding of only 10 targets.</p

Search efficiency <i>η</i> vs. power-law exponent <i>μ</i> in Lévy dust distributions.

<p>The searcher detected 10⁴ targets in a landscape with Lévy dust distributions of <i>N</i>_<i>t</i> = 10⁴ targets (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005774#sec007" target="_blank">Methods</a> section). Parameters are set as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005774#pcbi.1005774.g002" target="_blank">Fig 2</a>. High clustering of targets and nearly homogeneous landscapes correspond to <i>β</i> = 3 and <i>β</i> = 1.1, respectively. In all cases, <i>η</i> is maximized for <i>μ</i>_opt ≈ 2, with a slight decrease in the optimal value (i.e. enhanced superdiffusion) observed as <i>β</i> → 3.</p

Fractal patches obtained by combining two Lévy dust distributions.

<p>Search landscapes containing Lévy dust distributions located in <i>N</i>_<i>p</i> = 50 patches. Here, <i>N</i>_<i>t</i> = 50000 (1000 targets per patch), <i>β</i> = 3.0, <i>d</i>₀ = 2, <i>d</i>_max = <i>M</i>/10 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005774#pcbi.1005774.e001" target="_blank">Eq (1)</a>, and <i>r</i>₀ = 100, <i>r</i>_max = <i>M</i> = 10⁴, (A) <i>γ</i> = 1.1, (B) <i>γ</i> = 2.0, (C) <i>γ</i> = 2.5, (D) <i>γ</i> = 3.0, in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005774#pcbi.1005774.e002" target="_blank">Eq (2)</a>. For large <i>γ</i> the patches are so close that one cannot distinguish them only by visual inspection.</p

Lévy dust distribution of targets.

<p>Search landscapes containing Lévy dust distributions of <i>N</i>_<i>t</i> = 10⁴ targets (see main text), drawn from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005774#pcbi.1005774.e001" target="_blank">Eq (1)</a> with <i>d</i>₀ = 1, <i>d</i>_max = <i>M</i> = 10⁴, and (A) <i>β</i> = 1.1, (B) <i>β</i> = 2.0, (C) <i>β</i> = 2.5, and (D) <i>β</i> = 3.0. Larger values of <i>β</i> increase the degree of clustering of targets. The bouncing of coordinates technique applied to the <i>β</i> = 1.1 case results in a nearly homogeneous targets distribution.</p