A dynamical model exploring sensory integration in the insect central complex substructures

Abstract

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.

The greater wax moth, Galleria mellonella (L.) uses two different sensory modalities to evaluate the suitability of potential oviposition sites

“…This clearly shows that the chemical cues, although important, is not the only criteria in decision making process in insects. It is imperative that an insect can contextually integrate sensory signals to formulate appropriate behavioral responses 35 .…”

Extending the Functional Subnetwork Approach to a Generalized Linear Integrate-and-Fire Neuron Model

“…We anticipate that these two approaches may complement one another, wherein the direct network tuning accomplished by the FSA could be used to initialize tuning within the NEF. In our experience, the FSA can be used to select initial network parameters that aid subsequent parameter optimization (Pickard et al, 2020). As a next step, we plan to compare the accuracy and efficiency of networks tuned via the FSA with those tuned via the NEF.…”

“…However, the FSA enables the rapid, direct assembly of networks that can perform sophisticated tasks, such as entraining rhythmic output to periodic inputs (Nourse et al, 2018) or learning the appropriate force to apply to the environment , right "out of the box." Such networks also serve as good starting configurations for subsequent optimization (Pickard et al, 2020). As such, we find the FSA to be useful for designing computational models and robot controllers.…”

Neurodynamic modeling of the fruit flyDrosophila melanogaster

“…The central complex (CX) is an increasingly well-understood brain network whose activity precedes and strongly correlates with locomotion direction [29,44,73]. Our recent dynamical model of substructures within the CX demonstrated how it may balance the influence of multiple exteroceptive inputs to 'decide' on one resulting heading [46]. We will continue to expand this model and integrate its outputs with the local leg networks we implemented here to build a plausible model of how the nervous system gathers information from its environment, processes it in a context-dependent way, and directs ongoing behaviors via a distributed control system.…”