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The fractal and chaos are bound tightly, and their relevant researches are well-established. Few of them, however, concentrate on the research of the possibility of combining fractal and chaotic systems for generating multi-scroll chaotic attractors. This paper presents a novel non-equilibrium point chaotic system, exhibiting extremely rich and complex hidden behaviors including chaos, hyper-chaos, multi-scroll attractors, extreme multi-stability, and initial offset-boosting behavior. The proposed system is combined with fractal transformation to observe a new class of multi-scroll attractors such as multi-ring attractors, separated-scroll attractors, and nested attractors. Particularly, the first swallow-like attractors are found. Moreover, another efficient method to generate a different class of chaotic attractors applies parabola transformation and triangle transformation. Additionally, the spectrum entropy (SE) complexity is also employed to discuss the complexity of the proposed system before and after fractal, resulting in chaotic sequences with the fractal transformation that has higher complexity. Finally, we develop a hardware platform to implement the presented attractors before and after fractal in a way to confirm the accuracy of the numerical simulations, providing a theoretical basis for the next application in image encryption.

Choosing among spatially distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process, we reveal the occurrence of spontaneous and abrupt “critical” transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one among, the remaining options. This bifurcation process repeats until only one option—the one ultimately selected—remains. Thus, we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space–time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological contexts, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

Recent studies of the Central Complex in the brain of the fruit fly have identified neurons with activity that tracks the animal’s heading direction. These neurons are part of a neuronal circuit with dynamics resembling those of a ring attractor. The homologous circuit in other insects has similar topographic structure but with significant structural and connectivity differences. We model the connectivity patterns of two insect species to investigate the effect of these differences on the dynamics of the circuit. We illustrate that the circuit found in locusts can also operate as a ring attractor but differences in the inhibition pattern enable the fruit fly circuit to respond faster to heading changes while additional recurrent connections render the locust circuit more tolerant to noise. Our findings demonstrate that subtle differences in neuronal projection patterns can have a significant effect on circuit performance and illustrate the need for a comparative approach in neuroscience.

Insect navigation arises from the coordinated action of concurrent guidance systems but the neural mechanisms through which each functions, and are then coordinated, remains unknown. We propose that insects require distinct strategies to retrace familiar routes (route-following) and directly return from novel to familiar terrain (homing) using different aspects of frequency encoded views that are processed in different neural pathways. We also demonstrate how the Central Complex and Mushroom Bodies regions of the insect brain may work in tandem to coordinate the directional output of different guidance cues through a contextually switched ring-attractor inspired by neural recordings. The resultant unified model of insect navigation reproduces behavioural data from a series of cue conflict experiments in realistic animal environments and offers testable hypotheses of where and how insects process visual cues, utilise the different information that they provide and coordinate their outputs to achieve the adaptive behaviours observed in the wild.

[This corrects the article DOI: 10.3389/fnbot.2023.1211570.].

The central complex is a highly conserved insect brain region composed of morphologically stereotyped neurons that arborize in distinctively shaped substructures. The region is implicated in a wide range of behaviors and several modeling studies have explored its circuit computations. Most studies have relied on assumptions about connectivity between neurons based on their overlap in light microscopy images. Here, we present an extensive functional connectome of Drosophila melanogaster’s central complex at cell-type resolution. Using simultaneous optogenetic stimulation, calcium imaging and pharmacology, we tested the connectivity between 70 presynaptic-to-postsynaptic cell-type pairs. We identified numerous inputs to the central complex, but only a small number of output channels. Additionally, the connectivity of this highly recurrent circuit appears to be sparser than anticipated from light microscopy images. Finally, the connectivity matrix highlights the potentially critical role of a class of bottleneck interneurons. All data are provided for interactive exploration on a website. Some of the most evocative discoveries in neuroscience have been those of internal representations, such as neural activity patterns that represent which direction an animal is facing and its place in its surroundings. Understanding how neurons connect to one another to form ‘circuits’ is crucial to understanding how these circuits maintain such representations. Many of the design principles that underlie circuit function in the brains of fruit flies apply to other animals. However, fly brains are easier to study because genetic tools can be used on them to selectively activate and image the activity of specific types of neurons. By activating one type of neuron and imaging the activity of another that may be connected to it, we obtain what is called a functional ‘connectome’: a map of neural connectivity that identifies different pathways that information can flow along. A region of the fly brain called the central complex is involved in many important behaviors, including navigation and sleep. Researchers know about the types of neurons in the region and about how the activity of some of them changes during different behaviors. However, obtaining the connectome of the central complex would make it easier to understand how the central complex works. A technique called optogenetics allows specific types of neurons to be activated one at a time by shining light onto them. By imaging the activity of neurons that might be connected to an optogenetically activated neuron, Franconville et al. have now built an extensive – albeit still incomplete – map of the connections within the central complex of fruit flies. The map reveals two key bottlenecks in the central complex circuit. Firstly, a neuron type in a substructure called the protocerebral bridge controls a lot of the information flowing through the circuit. Secondly, the circuit appears to have very few true ‘output’ neuron types – Franconville et al. identified only one. These results suggest that however complicated the computations performed by the central complex circuit might be, the output of the circuit, which likely guides the fly’s actions, may be much simpler. Franconville et al. have compiled the mapping results into an interactive website that makes the neuroscientific data both freely available and easily explorable. As researchers perform more such experiments, the new data can be added to the map. This information can be used to constrain theories and inspire new ideas about how the central complex does what it does.

We introduce a bio-inspired navigation system for a robot to guide a social agent to a target location while avoiding static and dynamic obstacles. Robot navigation can be accomplished through a model of ring attractor neural networks. This connectivity pattern between neurons enables the generation of stable activity patterns that can represent continuous variables such as heading direction or position. The integration of sensory representation, decision-making, and motor control through ring attractor networks offers a biologically-inspired approach to navigation in complex environments. The navigation system is divided into perception, planning, and control stages. Our approach is compared to the widely-used Social Force Model and Rapidly Exploring Random Tree Star methods using the Social Individual Index and Relative Motion Index as metrics in simulated experiments. We created a virtual scenario of a pedestrian area with various obstacles and dynamic agents. The results obtained in our experiments demonstrate the effectiveness of this architecture in guiding a social agent while avoiding obstacles, and the metrics used for evaluating the system indicate that our proposal outperforms the widely used Social Force Model. Our approach points to improving safety and comfort specifically for human-robot interactions. By integrating the Social Individual Index and Relative Motion Index, this approach considers both social comfort and collision avoidance features, resulting in better human-robot interactions in a crowded environment.

Animal navigation is accomplished by a combination of landmark-following and dead reckoning based on estimates of self motion. Both of these approaches require the encoding of heading information, which can be represented as an allocentric or egocentric azimuthal angle. Recently, Ca correlates of landmark position and heading direction, in egocentric coordinates, were observed in the ellipsoid body (EB), a ring-shaped processing unit in the fly central complex (CX; Seelig and Jayaraman, 2015). These correlates displayed key dynamics of so-called ring attractors, namely: (1) responsiveness to the position of external stimuli; (2) persistence in the absence of external stimuli; (3) locking onto a single external stimulus when presented with two competitors; (4) stochastically switching between competitors with low probability; and (5) sliding or jumping between positions when an external stimulus moves. We hypothesized that ring attractor-like activity in the EB arises from reciprocal neuronal connections to a related structure, the protocerebral bridge (PB). Using recent light-microscopy resolution catalogs of neuronal cell types in the PB (Lin et al., 2013; Wolff et al., 2015), we determined a connectivity matrix for the PB-EB circuit. When activity in this network was simulated using a leaky-integrate-and-fire model, we observed patterns of activity that closely resemble the reported Ca phenomena. All qualitative ring attractor behaviors were recapitulated in our model, allowing us to predict failure modes of the putative PB-EB ring attractor and the circuit dynamics phenotypes of thermogenetic or optogenetic manipulations. Ring attractor dynamics emerged under a wide variety of parameter configurations, even including non-spiking leaky-integrator implementations. This suggests that the ring-attractor computation is a robust output of this circuit, apparently arising from its high-level network properties (topological configuration, local excitation and long-range inhibition) rather than fine-scale biological detail.

Insect-inspired sensor fusion algorithms have presented a promising avenue in the development of robust and efficient systems, owing to the insects' ability to process numerous streams of noisy sensory data. The ring attractor neural network architecture has been identified as a noteworthy model for the optimal integration of diverse insect sensors. Expanding on this, our research presents an innovative bio-inspired ring attractor neural network architecture designed to augment the performance of microsatellite attitude determination systems through the fusion of data from multiple gyroscopic sensors.Extensive simulations using a nonlinear model of the microsatellite, while incorporating specific navigational disturbances, have been conducted to ascertain the viability and effectiveness of this approach. The results obtained have been superior to those of alternative methodologies, thus highlighting the potential of our proposed bio-inspired fusion technique. The findings indicate that this approach could significantly improve the accuracy and robustness of microsatellite systems across a wide range of applications.

Head‐direction (HD) cells are a fundamental component in the hippocampal‐entorhinal circuit for spatial navigation and help maintain an internal sense of direction to anchor the orientation in space. A classical HD cell robustly increases its firing rate when the head is oriented toward a specific direction, with each cell tuned to only one direction. Although unidirectional HD cells are reported broadly across multiple brain regions, computation modelling has predicted the existence of multiple equilibrium states of HD network, which has yet to be proven. In this study, a novel HD variant of bipolar HD cells in the medial entorhinal cortex (MEC) are identified that exhibit stable double‐peaked directional tuning properties. The bipolar patterns remain stable in the darkness and across environments of distinct geometric shapes. Moreover, bipolar HD cells co‐rotate coherently with unipolar HD cells to anchor the external visual cue. The discovery reveals a new spatial cell type of bipolar HD cells, whose unique activity patterns may comprise a potential building block for a sophisticated local neural circuit configuration for the internal representation of direction. These findings may contribute to the understanding of how the brain processes spatial information by shedding light on the role of bipolar HD cells in this process. Head‐direction (HD) cells function as the brain's internal compass. A classic HD cells fire preferentially when the head is oriented towards a specific direction. This study identifies a novel type of bipolar HD cells in the medial entorhinal cortex with intrinsically stable double‐peaked directional tuning. Their presence adds a new potential building block for spatial navigation.