Data for 'Estimating orientation in Natural scenes: A Spiking Neural Network Model of the Insect Central Complex' (2024)

Data for paper published in <i>PLOS Computational Biology (Aug 2024)</i><b>Abstract</b>The central complex of insects contains cells, organised as a ring attractor, that encode head direction. The `bump' of activity in the ring can be updated by idiothetic cues and external sensory information. Plasticity at the synapses between these cells and the ring neurons, that are responsible for bringing sensory information into the central complex, has been proposed to form a mapping between visual cues and the heading estimate which allows for more accurate tracking of the current heading, than if only idiothetic information were used.In Drosophila, ring neurons have well characterised non-linear receptive fields. In this work we produce synthetic versions of these visual receptive fields using a combination of excitatory inputs and mutual inhibition between ring neurons. We use these receptive fields to bring visual information into a spiking neural network model of the insect central complex based on the recently published Drosophila connectome. Previous modelling work has focused on how this circuit functions as a ring attractor using the same type of simple visual cues commonly used experimentally. While we initially test the model on these simple stimuli, we then go on to apply the model to complex natural scenes containing multiple conflicting cues. We show that this simple visual filtering provided by the ring neurons is sufficient to form a mapping between heading and visual features and maintain the heading estimate in the absence of angular velocity input. The network is successful at tracking heading even when presented with videos of natural scenes containing conflicting information from environmental changes and translation of the camera.All code used for this project has been made publicly available on GitHub: https://github.com/stenti/stentiford_cx_ra.<br>Data required to run the code can be found here.mp4 files: all raw videos used as input to the model. '3rev_static' indicated videos recorded with the camera rotating for 3 revolutions in a stationary position. 'circling' idicates videos recorded using the spidercam robot either rotation on the spot 'static' or moving in a circle 'super'. (to be loaded by cx_ra_rn.py)pkl files: simple stimuli input that does not require preprocessing (to be loaded by cx_ra_rn.py)npy files: Weight matrices between different populations of cells (to be loaded by cx_ra_rn.py)<br>

本数据集关联发表于2024年8月《PLOS计算生物学》（PLOS Computational Biology）的研究论文。

**摘要**：昆虫中央复合体（central complex）中存在以环形吸引子（ring attractor）形式组织的细胞群，负责编码头部方向信息。该环形结构中的活动“峰”可通过自运动线索（idiothetic cues）与外部感官信息进行更新。此前有研究提出，这些细胞与负责将感官信息传入中央复合体的环形神经元（ring neurons）之间的突触可塑性，可在视觉线索与航向估计之间建立映射，相比仅使用自运动线索的情况，该映射能实现更精准的当前航向追踪。
在果蝇（Drosophila）中，环形神经元具有已被充分表征的非线性感受野（receptive fields）。本研究通过联合使用兴奋性输入与环形神经元间的相互抑制，构建了这类视觉感受野的仿真版本。我们将此类感受野用于将视觉信息传入基于最新发表的果蝇连接组（connectome）构建的昆虫中央复合体脉冲神经网络（spiking neural network）模型。
既往建模研究多聚焦于该环路如何以实验中常用的同类简单视觉线索发挥环形吸引子功能。本研究首先基于此类简单刺激对模型进行测试，随后将模型应用于包含多重冲突线索的复杂自然场景。研究结果表明，环形神经元提供的简易视觉滤波足以在航向与视觉特征之间建立映射，并在无角速度输入的情况下维持航向估计。即便输入包含环境变化与相机平移带来的冲突信息的自然场景视频，该网络仍能有效追踪航向。

本项目所用全部代码已公开至GitHub平台：https://github.com/stenti/stentiford_cx_ra。

运行代码所需的数据集可通过以下路径获取：
- MP4文件：模型所用全部原始输入视频。其中“3rev_static”指相机在静止位置旋转3圈的录制视频；“circling”指使用蜘蛛式相机机器人录制的视频，涵盖原地旋转的“static”子类与圆周运动的“super”子类（需通过cx_ra_rn.py加载）。
- PKL文件：无需预处理的简易刺激输入数据（需通过cx_ra_rn.py加载）。
- NPY文件：不同细胞群之间的权重矩阵（需通过cx_ra_rn.py加载）。