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A dynamical model exploring sensory integration in the insect central complex substructures.
Bioinspiration & Biomimetics ( IF 3.4 ) Pub Date : 2020-01-13 , DOI: 10.1088/1748-3190/ab57b6
S C Pickard 1 , R D Quinn , N S Szczecinski
Affiliation  

It is imperative that an animal has the ability to contextually integrate received sensory information to formulate appropriate behavioral responses. Determining a body heading based on a multitude of ego-motion cues and visual landmarks is an example of such a task that requires this context dependent integration. The work presented here simulates a sensory integrator in the insect brain called the central complex (CX). Based on the architecture of the CX, we assembled a dynamical neural simulation of two structures called the protocerebral bridge (PB) and the ellipsoid body (EB). Using non-spiking neuronal dynamics, our simulation was able to recreate in vivo neuronal behavior such as correlating body rotation direction and speed to activity bumps within the EB as well as updating the believed heading with quick secondary system updates. With this model, we performed sensitivity analysis of certain neuronal parameters as a possible means to control multi-system gains during sensory integration. We found that modulation of synapses in the memory network and EB inhibition are two possible mechanisms in which a sensory system could affect the memory stability and gain of another input, respectively. This model serves as an exploration in network design for integrating simultaneous idiothetic and allothetic cues in the task of body tracking and determining contextually dependent behavioral outputs.

中文翻译:

探索昆虫中央复杂子结构中的感官整合的动力学模型。

至关重要的是,动物必须能够根据上下文整合接收到的感官信息,以制定适当的行为反应。基于多种自我运动线索和视觉界标确定身体朝向是此类任务的一个示例,该任务需要这种依赖于上下文的集成。此处介绍的工作在昆虫大脑中模拟了一种感觉统合体,称为中央复合体(CX)。基于CX的体系结构,我们组装了两种结构的动力学神经仿真,这两种结构分别称为前脑桥(PB)和椭球体(EB)。使用非加扰的神经元动力学,我们的仿真能够重现体内神经元行为,例如将人体旋转方向和速度与EB中的活动颠簸相关联,以及通过快速的辅助系统更新来更新前进方向。使用此模型,我们对某些神经元参数进行了敏感性分析,作为在感觉整合过程中控制多系统增益的一种可能方法。我们发现记忆网络中突触的调制和EB抑制是感觉系统分别影响记忆稳定性和另一个输入的增益的两种可能机制。此模型可作为网络设计的探索,以将惯常和同速线索同时整合到人体跟踪任务中,并确定上下文相关的行为输出。我们发现记忆网络中突触的调制和EB抑制是感觉系统分别影响记忆稳定性和另一个输入的增益的两种可能机制。该模型可作为网络设计的探索,以将惯常和同速线索同时整合到人体跟踪任务中,并确定上下文相关的行为输出。我们发现记忆网络中突触的调制和EB抑制是感觉系统分别影响记忆稳定性和另一个输入的增益的两种可能机制。该模型可作为网络设计的探索,以将惯常和同速线索同时整合到人体跟踪任务中,并确定上下文相关的行为输出。
更新日期:2019-11-01
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