Open-source contributor guide
Meta-brain models are hybrid models that include both a representation-free component and a representation-rich component. As we can see from the diagram above, this relationship mimics the neocortical-thalamic system relationship of the mammalian brain, and can be made spatially explicit (the representation-rich component can "encase" or be "layered" on top of the representation-free component).
Examples of a representation-free model includes Braitenberg Vehicles or Neural Networks. Examples of a representation-rich model (from our library) includes Contextual Geometric Structures or Ideological Connectionist models. We have a library of possible model configurations, or incorporate your own!
This project involves three aims: creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology that is flexible enough to input/output methodology that is flexible enough to accommodate both behavioral and social phenomena.
General References:
Brooks, R. (1991). Intelligence without Representation. Artificial Intelligence, 47, 139-159.
Cesario, J., Johnson, D.J., and Eisthen, H.L. (2020). Your Brain Is Not an Onion With a Tiny Reptile Inside. Current Directions in Psychological Science, 1–6.
Cisek, P. (2019). Resynthesizing behavior through phylogenetic refinement. Attention, Perception, and Psychophysics, 81, 2265-2287.
Hao, K. (2020). A debate between AI experts shows a battle over the technology’s future. MIT Technology Review, March 27.
Wolff, M. and Vann, S.D. (2019). The Cognitive Thalamus as a Gateway to Mental Representations. Journal of Neuroscience, 39(1), 3-14. doi:10.1523/JNEUROSCI.0479-18.2018
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