Understanding the Emergence of Modularity in Neural Systems

By John A. Bullinaria. 2007

47 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “It is also important to note that EPANN do not have the neurophysiological constraints of biological systems, e.g., short neural connections, sparsity, brain size limits, etc., which impacts the evolution of particular architectures such as modularity (Bullinaria, 2007b, 2009a).”
Imitative and Direct Learning as Interacting Factors in Life History Evolution (2017, unknown): “These details capture many of the key aspects of real animal populations, and although they clearly involve a great deal of simplification, they constitute a manageable level of approximation that has proved to be effective in numerous previous studies [14, 15, 16, 17, 18].”
The Importance of Neurophysiological Constraints for Modelling the Emergence of Modularity (methodology): “Since it seems not to be crucial which evolutionary process is used, this paper will adopt the computationally simpler approach of Bullinaria (2007b).”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, background): “From an evolutionary and developmental perspective, modularity is considered to have brought about important computational advantages (Bullinaria, 2007).”
Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans (2009, background): “However, it remains controversial in the brain networks, with arguments concerning the existence and emergence of the modular organization [62].”