Random Walk Revisited: Quantification and Comparative Analysis of Drosophila Walking Trajectories.

Walking trajectory is frequently measured to assess animal behavior. Air-supported spherical treadmills have been developed for real-time monitoring of animal walking trajectories. However, current systems for mice mainly employ computer mouse microcameras (chip-on-board sensors) to monitor ball motion, and these detectors exhibit technical issues with focus and rotation scale. In addition, computational methods to analyze and quantify the "random walk" of organisms are under-developed. In this work, we overcame the hurdle of frame-to-signal translation to develop a treadmill system with camera-based detection. Moreover, we generated a package of mathematical methods to quantify distinct aspects of Drosophila walking trajectories. By extracting and quantifying certain features of walking dynamics with high temporal resolution, we found that depending on their internal state, flies employ different walking strategies to approach environmental cues. This camera-based treadmill system and method package may also be applicable to monitor the walking trajectories of other diverse animal species.

Interneuron Diversity: Toward a Better Understanding of Interneuron Development In the Olfactory System.

The Drosophila olfactory system is an attractive model for exploring the wiring logic of complex neural circuits. Remarkably, olfactory local interneurons exhibit high diversity and variability in their morphologies and intrinsic properties. Although olfactory sensory and projection neurons have been extensively studied of development and wiring; the development, mechanisms for establishing diversity, and integration of olfactory local interneurons into the developing circuit remain largely undescribed. In this review, we discuss some challenges and recent advances in the study of Drosophila olfactory interneurons.

Publisher Correction: Diverse populations of local interneurons integrate into the Drosophila adult olfactory circuit.

The original version of this Article contained errors in Figs. 4 and 6. In Fig. 4, panel a, text labels UAS-FLP and LexAop2>stop>myr::smGdP-HA were shifted upwards during typesetting of the figure, and in Fig. 6, panel h, the number 15 was incorrectly placed on the heat map scale. These have now been corrected in both the PDF and HTML versions of the Article.

Diverse populations of local interneurons integrate into the Drosophila adult olfactory circuit.

Drosophila olfactory local interneurons (LNs) in the antennal lobe are highly diverse and variable. How and when distinct types of LNs emerge, differentiate, and integrate into the olfactory circuit is unknown. Through systematic developmental analyses, we found that LNs are recruited to the adult olfactory circuit in three groups. Group 1 LNs are residual larval LNs. Group 2 are adult-specific LNs that emerge before cognate sensory and projection neurons establish synaptic specificity, and Group 3 LNs emerge after synaptic specificity is established. Group 1 larval LNs are selectively reintegrated into the adult circuit through pruning and re-extension of processes to distinct regions of the antennal lobe, while others die during metamorphosis. Precise temporal control of this pruning and cell death shapes the global organization of the adult antennal lobe. Our findings provide a road map to understand how LNs develop and contribute to constructing the olfactory circuit.

Circuit variability interacts with excitatory-inhibitory diversity of interneurons to regulate network encoding capacity.

Local interneurons (LNs) in the Drosophila olfactory system exhibit neuronal diversity and variability, yet it is still unknown how these features impact information encoding capacity and reliability in a complex LN network. We employed two strategies to construct a diverse excitatory-inhibitory neural network beginning with a ring network structure and then introduced distinct types of inhibitory interneurons and circuit variability to the simulated network. The continuity of activity within the node ensemble (oscillation pattern) was used as a readout to describe the temporal dynamics of network activity. We found that inhibitory interneurons enhance the encoding capacity by protecting the network from extremely short activation periods when the network wiring complexity is very high. In addition, distinct types of interneurons have differential effects on encoding capacity and reliability. Circuit variability may enhance the encoding reliability, with or without compromising encoding capacity. Therefore, we have described how circuit variability of interneurons may interact with excitatory-inhibitory diversity to enhance the encoding capacity and distinguishability of neural networks. In this work, we evaluate the effects of different types and degrees of connection diversity on a ring model, which may simulate interneuron networks in the Drosophila olfactory system or other biological systems.

Phase locking route behind complex periodic windows in a forced oscillator.

Chaotic systems have complex reactions against an external driving force; even in cases with low-dimension oscillators, the routes to synchronization are diverse. We proposed a stroboscope-based method for analyzing driven chaotic systems in their phase space. According to two statistic quantities generated from time series, we could realize the system state and the driving behavior simultaneously. We demonstrated our method in a driven bi-stable system, which showed complex period windows under a proper driving force. With increasing periodic driving force, a route from interior periodic oscillation to phase synchronization through the chaos state could be found. Periodic windows could also be identified and the circumstances under which they occurred distinguished. Statistical results were supported by conditional Lyapunov exponent analysis to show the power in analyzing the unknown time series.