Braitenberg Vehicles as Developmental Neurosimulation

Journal article

Stefan Dvoretskii, Ziyi Gong, Ankit Gupta, Jesse Parent, Bradly Alicea
CogNet, Artificial Life, vol. 28(3), 2022, pp. 369–395

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APA   Click to copy
Dvoretskii, S., Gong, Z., Gupta, A., Parent, J., & Alicea, B. (2022). Braitenberg Vehicles as Developmental Neurosimulation. Artificial Life, 28(3), 369–395.

Chicago/Turabian   Click to copy
Dvoretskii, Stefan, Ziyi Gong, Ankit Gupta, Jesse Parent, and Bradly Alicea. “Braitenberg Vehicles as Developmental Neurosimulation.” Artificial Life 28, no. 3. CogNet (2022): 369–395.

MLA   Click to copy
Dvoretskii, Stefan, et al. “Braitenberg Vehicles as Developmental Neurosimulation.” Artificial Life, vol. 28, no. 3, 2022, pp. 369–95, doi:10.1162/artl_a_00384.

BibTeX   Click to copy

  title = {Braitenberg Vehicles as Developmental Neurosimulation},
  year = {2022},
  issue = {3},
  journal = {Artificial Life},
  pages = {369–395},
  series = {CogNet},
  volume = {28},
  doi = {10.1162/artl_a_00384},
  author = {Dvoretskii, Stefan and Gong, Ziyi and Gupta, Ankit and Parent, Jesse and Alicea, Bradly}


Abstract Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental Braitenberg vehicles (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, a multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach.