When Mayank Mehta, a neurophysicist at the University of California (UC), Los Angeles, compared the activity of place cells in rats running along a real, linear track with place cell activity in the rats running in virtual reality, he saw some surprising differences.
Each image series consisted of a sequence of 100 panoramic images taken along a linear track with a length of 1 m in natural outdoor environments.
Finite numbers of place cells were simulated with place field centers x c which were either uniformly distributed along a linear track with equal spacing or randomly sampled from a uniform distribution over the track.
To replicate the results of Harris et al. (2003), we simulated constant speed movement along a linear track, with lap by lap running speeds drawn from a normal distribution with mean 35 cm/s and standard deviation of 15 cm/s.
In addition, we applied the same analysis to another dataset simulated with k = 0. To simulate remapping in the coordinated assembly model, we simulated spiking activity for a population of 300 cells on a linear track with weights as described in Supplementary file 1, Appendix: A4.
By utilizing the feedback linearization technique, a linear tracking error system is established with prescribed references.
A novel linear tracking integrator (LTI) with integral compensation is proposed for efficient integral estimation from a contaminated measurement with a constant or time-varying bias.
A complex modal superposition is adopted to decouple the equations of motion of the linear track model with a non-proportional spatial distribution of viscous damping.
Thus, linear tracks associated with back-to-back objects overlap in the space-frequency (SF) domain, making it tedious to design SF filter mask functions to resolve such objects.
As can be seen in the figure, these pairs of linear tracks associated with the two objects overlap in the SF plane, making it tedious to design SF filter mask functions to resolve them [14].
