Abstract
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.
Highlights
Neurorobotics attempts to derive inspiration from neuroscience on how the brain solves problems in order to develop robust and adaptive artificial agents
The neuronal activity “bump” is maintained even when the visual stimulus is removed, and it moves relative to the no longer visible cue as the animal walks in darkness (Seelig and Jayaraman, 2015). This neuronal activity appears to constitute an internal encoding of heading, which is strongly reminiscent of the hypothetical ring attractor (Amari, 1977) proposed by Skaggs et al (1995) to account for the “head direction” cells of rats (Taube et al, 1990)
I subsequently illustrate that combining insights from reverse engineering with simulations allows us to explore the circuit’s function and identify some notable differences from classic ring attractor models, which may contribute to the stability and flexibility of its function
Summary
Neurorobotics attempts to derive inspiration from neuroscience on how the brain solves problems in order to develop robust and adaptive artificial agents. Phenomenological models attempt to reproduce the mapping of inputs to outputs while being only weakly constrained with respect to the actual neuronal circuit’s architecture, admitting a range of possible implementations This approach has the potential to provide inspiration for hypothesis formulation and for focusing further research but does not unravel the actual neuronal circuits of biological organisms. Another approach for analyzing neuronal circuits is to simulate part of the connectome in order to study the circuit’s function. I present here an example of this approach by reverse engineering the head direction circuit of the fruit fly and utilizing simulations of a situated robotic agent to characterize the circuit’s performance
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