Place-cell capacity and volatility with grid-like inputs
Man Yi Yim,
Lorenzo A Sadun,
Ila R Fiete,
Thibaud Taillefumier
Affiliations
Man Yi Yim
Center for Theoretical and Computational Neuroscience, University of Texas, Austin, United States; Department of Neuroscience, University of Texas, Austin, United States; Department of Brain and Cognitive Sciences and McGovern Institute, MIT, Austin, United States
Center for Theoretical and Computational Neuroscience, University of Texas, Austin, United States; Department of Brain and Cognitive Sciences and McGovern Institute, MIT, Austin, United States
Center for Theoretical and Computational Neuroscience, University of Texas, Austin, United States; Department of Neuroscience, University of Texas, Austin, United States; Department of Mathematics and Neuroscience, The University of Texas, Austin, United States
What factors constrain the arrangement of the multiple fields of a place cell? By modeling place cells as perceptrons that act on multiscale periodic grid-cell inputs, we analytically enumerate a place cell’s repertoire – how many field arrangements it can realize without external cues while its grid inputs are unique – and derive its capacity – the spatial range over which it can achieve any field arrangement. We show that the repertoire is very large and relatively noise-robust. However, the repertoire is a vanishing fraction of all arrangements, while capacity scales only as the sum of the grid periods so field arrangements are constrained over larger distances. Thus, grid-driven place field arrangements define a large response scaffold that is strongly constrained by its structured inputs. Finally, we show that altering grid-place weights to generate an arbitrary new place field strongly affects existing arrangements, which could explain the volatility of the place code.
