A model of cue integration as vector summation in the insect brain

Abstract

Ball-rolling dung beetles are known to integrate multiple cues in order to facilitate their straight-line orientation behaviour. Recent work has suggested that orientation cues are integrated according to a vector sum, that is, compass cues are represented by vectors and summed to give a combined orientation estimate. Further, cue weight (vector magnitude) appears to be set according to cue reliability. This is consistent with the popular Bayesian view of cue integration: cues are integrated to reduce or minimize an agent's uncertainty about the external world. Integration of orientation cues is believed to occur at the input to the insect central complex. Here, we demonstrate that a model of the head direction circuit of the central complex, including plasticity in input synapses, can act as a substrate for cue integration as vector summation. Further, we show that cue influence is not necessarily driven by cue reliability. Finally, we present a dung beetle behavioural experiment which, in combination with simulation, strongly suggests that these beetles do not weight cues according to reliability. We suggest an alternative strategy whereby cues are weighted according to relative contrast, which can also explain previous results.

Estimating orientation in Natural scenes: A Spiking Neural Network Model of the Insect Central Complex

“…As our focus was on how visual ring neuron inputs influence the network rather than specifically how the bump is formed or maintained, we constrained the model to the essential underlying architecture and connections required to produce the expected ring attractor dynamics, whilst using cell types and connections from up-to-date connectome data [13, 14]. Notably, PEG cells were excluded which were instrumental in previous work for maintaining bump persistence [20–23, 25, 27], because their function was replaced by direct self-recurrent connections between EPG cells identified in the connectome analysis [14]. As the bump properties remain similar in our model, we now need to look to experiments as to whether PEG cells and self-recurrent EPG activity are redundant mechanisms or whether each has it’s own influence over the circuit.…”

“…Recent Drosophila connectome data has revealed self-recurrent connections between excitatory EPG cells that likely ensure bump persistence [13, 14, 26]. This is in contrast to proposals in previous models where reciprocal connections between EPG cells and PEG cells would maintain the bump [20–23, 25, 27], however PEG to EPG connections are actually very weak [14]. Global inhibition is provided by two further cells types: Δ7 which functionally deliver strong inhibitory input to EPG cells on the ring that are opposite from the location of the bump ([13]; Fig 1D), and GABAergic ring neurons which have characteristic ring-shaped axonal projections enabling inhibition of EPG cells around the EB ([28]; Fig 1E).…”

“…The stability of a single bump relies on interactions between an excitatory population with recurrent connections and global inhibition which prevents runaway activity. Several computational models have been proposed to demonstrate how this circuit can use attractor dynamics to encode heading direction [10,[20][21][22][23][24][25][26][27]. These models all include EPG and PEN cells (or equivalent) as the excitatory component, with reciprocal connections between the two populations forming the bump by propagating activity around the ring network (Fig 1C).…”

“…Several computational models have been proposed to demonstrate how this circuit can use attractor dynamics to encode heading direction [10, 20–27]. These models all include EPG and PEN cells (or equivalent) as the excitatory component, with reciprocal connections between the two populations forming the bump by propagating activity around the ring network (Fig 1C).…”

“…Previous models of the insect central complex have represented visual input with several methods: simply by mapping the horizontal position of the visual cue (bright vertical bar) onto the 16 EPG cells and stimulating the appropriate PEN cell to move the bump [22, 26]; down sampling the visual scene and applying Gaussian filtering to approximate ring neuron receptive fields, then using the pixel intensity to represent inhibitory ring neuron activity [33]; and representing visual landmarks using multiple ring neuron populations each selectively tuned to the position of a specific landmark in the visual field [25, 27]. Here we present a generalized, minimal spiking neural network model of the insect central complex only including essential connections and cell types, using ring neurons with Drosophila -like visual receptive fields to tether the heading estimate to features of a natural visual scene.…”