Discovering Grid-Cell Models Through Evolutionary Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Wang:2016:CEC,

• author = "Lin Wang and Bo Yang and Jeff Orchard",
• title = "Discovering Grid-Cell Models Through Evolutionary Computation",
• booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)",
• year = "2016",
• editor = "Yew-Soon Ong",
• pages = "4683--4690",
• address = "Vancouver",
• month = "24-29 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-5090-0623-6",
• DOI = "doi:10.1109/CEC.2016.7744388",
• abstract = "One of the main tasks in neuroscience research is to interpret the activity of neurons. Given some neuroscientific data, such as spike trains, one tries to decipher how the activity of the neurons relate to the outside world and/or the behaviour of the animal. The discovery of place cells and grid cells are great examples - discoveries that garnered a Nobel Prize in 2014. However, the spatial patterns exhibited by such cells are only the beginning of our understanding of spatial representation in the brain. In this paper, we apply an evolutionary algorithm to discover spatial patterns exhibited in cells from the entorhinal cortex to see (1) if we can automatically deduce an accurate model for the hexagonal-grid pattern, and (2) if we can discover a more general model that also incorporates grid-cell-like variants that have been observed, but not understood.",
• notes = "WCCI2016",

}

Genetic Programming entries for Lin Wang Bo Yang Jeff Orchard

Citations