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Assessing the suitability of solutions among these congestion management measures (CMMs) requires a realistic modeling of grid operation, energy system structures and the behavior of the owners of DER and flexibility options.
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Finally in Sect. 5 we discuss natural extensions of our work, and its relevance to further models of grid cell firing.
The analysis of the corresponding travelling waves, with hexagonal structure, in two spatial dimensions is more challenging, though is an important requirement for a complete model of grid cell firing.
We investigated the relationship between rate coded spatial computations and nested gamma oscillations in attractor network models of grid firing.
Within the context of specific models of grid to place cell transformations we also show effects on spatial coding of selectively lesioning grid modules.
Finally, we examine possible deficits in spatial behavior that will obtain upon inactivating grid modules in the context of specific models of grid cells readout.
Here we describe a model of grid cell formation that accounts for both these aspects of spatial cognition, in a unitary perspective on the mEC network.
These dynamics are absent from other attractor network models of grid firing (Fuhs and Touretzky, 2006; Guanella et al., 2007; Burak and Fiete, 2009).
Above we used Lagrange multipliers to enforce the constraint on resolution and to bound the scale ratios to avoid ambiguity while minimizing the number of neurons required by a winner-take-all decoding model of grid systems.
Attractor network models of grid formation readily produce triangular lattices (Burak and Fiete, 2009); our analysis suggests that this architecture is functionally beneficial in reducing the required number of neurons.
Models of grid cell formation largely describe their firing in terms of self-motion [ 5, 19 22], and the integration of self-motion as the animal explores the environment (path integration) is one way in which the relative position of points in space can be discerned.
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