The recent discovery of the head-direction (HD) system in fruit flies has provided unprecedented insights into the neural mechanisms of spatial orientation. Despite the progress, the neural substance of global inhibition, an essential component of the HD circuits, remains controversial. Some studies suggested that the ring neurons provide global inhibition, while others suggested the Δ7 neurons. In the present study, we provide evaluations from the theoretical perspective by performing systematic analyses on the computational models based on the ring-neuron (R models) and Δ7-neurons (Delta models) hypotheses with modifications according to the latest connectomic data. We conducted four tests: robustness, persistency, speed, and dynamical characteristics. We discovered that the two models led to a comparable performance in general, but each excelled in different tests. The R Models were more robust, while the Delta models were better in the persistency test. We also tested a hybrid model that combines both inhibitory mechanisms. While the performances of the R and Delta models in each test are highly parameter-dependent, the Hybrid model performed well in all tests with the same set of parameters. Our results suggest the possibility of combined inhibitory mechanisms in the HD circuits of fruit flies.
15Spatial orientation plays a crucial role in animal navigation. Recent studies of tethered 16Drosophila melanogaster (fruit fly) in a virtual reality setting showed that the 17 orientation is encoded in the form of an activity bump, i.e. localized neural activity, in 18 the torus-shaped ellipsoid body (EB). Moreover, a fly can maintain working memory of 19 its orientation with a stable and persistent activity bump in the absence of any visual 20 cue, and update the memory in accordance with changes of the body orientation by 21shifting the location of the bump. Although the neural circuit that is responsible for 22shifting the bump has been extensively studied lately, how the nervous system shifts 23 the bump while maintains its stability and persistence is poorly understood. We 24 investigated this question using free moving fruit flies in a spatial orientation memory 25 task, and manipulated two EB subsystems, the P circuit, which has been suggested 26 for the stabilization function, and the C circuit, which has been suggested for the 27 updating function but was largely overlooked. We discovered that overactivating either 28 circuit produced distinct behavioral deficits, confirming that the two circuits play 29 important but different roles in the orientation working memory. Furthermore, 30 suppressing either circuit disrupted the memory, suggesting that the C or P circuit 31 alone is not sufficient to maintain the orientation working memory. We reproduced the 32 observations with a spiking neural network model of EB and demonstrated that spatial 33 orientation working memory requires coordinated activation of the stabilizing and 34 updating neural processes in different movement modes. 35 36 Keywords 37 Central complex, ellipsoid body, spatial orientation memory, working memory, Buridan's 38 paradigm, Drosophila melanogaster 39 40 Introduction 41Maintaining spatial orientation is a crucial cognitive capability required for animal 42 navigation [1,2], and understanding the detailed neural mechanisms of spatial orientation 43 is of great interest to researchers in the fields of neurobiology [3][4][5] or neuromorphic 44 engineering [6][7][8]. In recent years, significant progress has been made in identifying the 45 neural circuits that support spatial orientation [9] in the central complex of Drosophila 46 melanogaster [10,11]. The central complex has long been associated with short-term 47Recently, a model of the EB-PB circuits proposed in Su et al., (2017) was built strictly 64 based on connectomic data and provided a detailed picture of the neural circuit 65 interactions underlying spatial orientation and its working memory [15]. The model 66suggests the involvement of two sets of coupled circuits that connect the EB and PB. One 67 set that consists of EIP (or E-PG) and PEI neurons forms symmetric recurrent connections, 68 and the other set that consists of EIP and PEN (or P-EN) forms asymmetric recurrent 69 4 connections [20,21]. The symmetric circuit, named the C circuit in this paper, forms an 70 attract...
Identifying the direction of signal flows in neural networks is important for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in more than 15 neuropils of a Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained only by information specific to nodes, the branch points on the skeleton, and includes both Soma Features (which contain spatial information from a given node to a soma) and Local Features (which contain morphological information of a given node). After including the spatial correlations between nodal polarities, our NPIN provided extremely high accuracy (>96.0%) for the classification of neuronal polarity, even for complex neurons with more than two dendrite/axon clusters. Finally, we further apply NPIN to classify the neuronal polarity of neurons in other species (Blowfly and Moth), which have much less neuronal data available. Our results demonstrate the potential of NPIN as a powerful tool to identify the neuronal polarity of insects and to map out the signal flows in the brain’s neural networks if more training data become available in the future.
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