Continuous Attractor Network Model for Conjunctive Position-by-Velocity Tuning of Grid Cells
Figure 6
The network is able to perform accurate path-integration even when the firing response is nonlinear in the input and the velocity input is of finite resolution (parameters: ).
(A) Snapshot of the network activity at one example step in the simulation. The firing of the units in the network saturates due to nonlinearity of the transfer function; (B) Firing maps of the units, as a function of the actual position and velocity of the simulated rat, show that the top two units are conjunctive grid units while the unit at the bottom is a pure positional grid unit. The coordinate in the neural space is indicated at the top of each panel. The spacing is 30 cm, determined by the parameter
put in the simulation. Non-sampled bins are represented by white color.