Anselmi, Fabio, Benedetta Franceschiello, Micah M. Murray, and Lorenzo Rosasco. “A computational model for grid maps in neural populations.” arXiv preprint arXiv:1902.06553 (2019).

The following content is extracted from Anselmi 2019.

Anselmi, Fabio, Benedetta Franceschiello, Micah M. Murray, and Lorenzo Rosasco. “A computational model for grid maps in neural populations.” arXiv preprint arXiv:1902.06553 (2019).

Since their discovery in 2005 grid cells have played a key-role in understanding how different species’ brains dynamically represent an animal’s position in space. Despite more then a decade of interest from a large number of investigators, a universally accepted model of how grid cells receptive fields emerge is still lacking.

In this study Anselmi et al. 2019 provides a new and simple theoretical and computational framework to explain how grid cells could possibly organize. We propose a novel formulation of the encoding problem through the modern Frame Theory language, providing new insights about the optimality of hexagonal grid receptive fields and overcoming some crucial limitations of the current attractor and oscillatory models. Moreover, they demonstrate that this same encoding strategy can generalize from spatial to more abstract information.

Fig from Anselmi et al. 2019.

For further info, please read the paper Anselmi et al. 2019

Anselmi, Fabio, Benedetta Franceschiello, Micah M. Murray, and Lorenzo Rosasco. “A computational model for grid maps in neural populations.” arXiv preprint arXiv:1902.06553 (2019).