Developmental Braitenberg Vehicles

Co-Authors: Bradly Alicea, Stefan Dvoretskii, Ziyi Gong, and Jesse Parent

Abstract

Neuroethology is the study of brain and behavior in the context of an animal’s natural environment [1]. In behavioral simulations, this naturalism can be approximated through interdependencies among brain, body, and environment. Our approach is particularly interesting to brain scientists because simulated environments allow for environmental and neurological components of a naturalistic interaction to be both specified and controlled. We use a type of Braitenberg Vehicle [2] called Developmental Braitenberg Vehicles (dBVs) to model the developmental aspects of an embodied artificial nervous system. As models of developmental origins, dBVs extend the understanding and functional context of original Braitenberg Vehicle behaviors. These behaviors range from phototropisms and sensorimotor coordination to more complex behaviors that resemble emotional valence and acquired knowledge. As the preview of a forthcoming paper from our group on this topic [3], we propose three distinct approaches to dBVs in software. These include a model based on genetic algorithms, a multisensory Hebbian learning model, and a multi-agent (dBV collective) model. Our software approaches can be used to study many different behavioral scenarios. Three instances of these involve optimized spatial cognition (genetic algorithm model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches).

[1] Datta, S.R., Anderson, D.J., Branson, K., Perona, P., and Leifer, A. (2019). Computational Neuroethology: a call to action. Neuron, 104(1), P11-P24.

[2] Braitenberg, V. (1984). Vehicles: experiments in synthetic Psychology. MIT Press, Cambridge, MA.

[3] Dvoretskii, S., Gong, Z., Gupta, A., Parent, J., and Alicea, B. (2020). Braitenberg Vehicles as Developmental Neurosimulation.

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