Highlights d Anesthetizing ants affects their path integration memory d Anesthesia degrades the distance, but not the direction, memory of the ants d The same effect is replicated by a path integration model of the central complex d The results argue against recurrently maintained activity as the memory substrate
Recent studies of the Central Complex in the brain of the fruit fly Drosophila melanogaster have identified neurons with localised activity that tracks the animal's heading direction. These neurons are part of a neuronal circuit with dynamics resembling those of a ring attractor. Other insects have a homologous circuit sharing a generally similar topographic structure but with significant structural and connectivity differences. In this study, we model the precise connectivity patterns in two insect species to investigate the effect of the differences on the dynamics of the circuit. We illustrate that the circuit found in locusts can also operate as a ring attractor and we explore the role and robustness of the connectivity parameters. We identify differences that enable the fruit fly circuit to respond faster to changes of heading while they render the locust circuit more tolerant to noise. Our findings demonstrate that subtle differences in neuronal projection patterns can have a significant effect on the circuit performance and emphasise the need for a comparative approach in neuroscience. 7 distractions [6], but is also essential for the more complex navigational process of path integration 8 (or dead reckoning) which enables central-place foragers to return directly to their nest after long 9 and convoluted outward paths [7][8][9][10]. While the neural basis underlying these navigation strategies 10 are not known in detail, a brain region called the central complex (CX) is implicated in many 11 navigation related processes. 12The CX of the insect brain is an unpaired, midline-spanning set of neuropils that consist of 13 the protocerebral bridge (PB), the ellipsoid body (also called lower division of the central body), 14 the fan-shaped body (also called upper division of the central body) and the paired noduli. These 15 neuropils and their characteristic internal organisation in vertical slices, combined with horizontal 16 1 layers are highly conserved across insects. This regular neuroarchitecture is generated by sets of 17 columnar cells, innervating individual slices, as well as large tangential neurons, innervating entire 18 layers. The structured projection patterns of columnar cells result in the PB being organised in 16 19 or 18 contiguous glomeruli and the ellipsoid body (EB) in 8 adjoined tiles. 20Crucially, the CX is of key importance for the computations required to derive a heading 21 signal [2,6,[11][12][13][14]. In locusts (Schistocerca gregaria), intracellular recordings have revealed a 22 neuronal layout that topographically maps the animal's orientation relative to simulated skylight 23 cues, including polarized light and point sources of light [15][16][17]. Calcium imaging of columnar 24 neurons connecting the EB and the PB (E-PG neurons) in the fruit fly Drosophila melanogaster, 25 revealed that the E-PG neuronal ensemble maintains localised spiking activity -commonly called 26 an activity 'bump' -that moves from one group of neurons to the next as the animal rotates 27 with respect to its...
Understanding neuronal circuits that have evolved over millions of years to control adaptive behavior may provide us with alternative solutions to problems in robotics. Recently developed genetic tools allow us to study the connectivity and function of the insect nervous system at the single neuron level. However, neuronal circuits are complex, so the question remains, can we unravel the complex neuronal connectivity to understand the principles of the computations it embodies? Here, I illustrate the plausibility of incorporating reverse engineering to analyze part of the central complex, an insect brain structure essential for navigation behaviors such as maintaining a specific compass heading and path integration. I demonstrate that the combination of reverse engineering with simulations allows the study of both the structure and function of the underlying circuit, an approach that augments our understanding of both the computation performed by the neuronal circuit and the role of its components.
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