Global inhibition in head-direction neural circuits: a systematic comparison between connectome-based spiking neural circuit models

Abstract

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.

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

“…and most other previous models are biased towards the Drosophila circuit [10,22,25,26]. However the real power of computational approaches is that they allow us to explore questions about other insect species and investigate how behavioural specialisms may come about from this conserved circuitry.…”

“…For the bump of activity to be maintained there must be recurrent connections back onto the currently active EPG cell. 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].…”

“…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).…”

“…Global inhibition is provided by two further cells types: ∆7 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). A combination of both of these inhibitory inputs has been shown to produce a more stable attractor network than either inhibitory source alone [26].…”

“…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.…”