Browsing by Subject "Grid cells"

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Grid cell attractor networks: development and implications
(2015-12) Widloski, John Eric; Fiete, Ila; Marder, Michael P., 1960-; Gordon, Vernita; Pillow, Jonathan; Swinney, Harry
At the foundation of our ability to plan trajectories in complex terrain is a basic need to establish one’s positional bearings in the environment, i.e., to self-localize. How does the brain perform self-localization? How does a net- work of neurons conspire to solve this task? How does it self organize? Given that there might be multiple solutions to this problem, with what certainty can we say that any such model faithfully captures the neural structure and dynamics as it exists in the brain? This thesis presents a collection of three theoretical works aimed at addressing these problems, with a particular focus on biological plausibility and amenability to testing experimentally. I first introduce the context within which the work in the thesis is situ- ated. Chapter 1 provides a framework for understanding algorithmically how the brain might solve the problem of self-localization and how a neural circuit could be organized to perform self-localization based on the integration of self-motion cues, an operation known as path integration. We also introduce the neurobiology that underlies self-localization, with special emphasis on the cell types found in and around the hippocampus. We discuss the case that a particular class of cells – grid cells – subserve path integration, because of their peculiar spatial response properties and their anatomical positioning as the recipients of self-motion information. Continuous attractor models are introduced as the favored description of the grid cell circuit. Key open questions are introduced as motivation for the subsequently described work. I next focus on the question of how the grid cell circuit may have organized. In Chapter 2, it is demonstrated that an unstructured immature neural network, when subjected to biologically plausible inputs and learning rules, can learn to produce grid-like spatial responses and perform path integration. This model makes a number of predictions for experiment which are described at length. In Chapter 3, I describe a theoretically motivated experimental probe of the organization and dynamics of the grid cell circuit. The proposed experiment relies on sparse neural recordings of grid cells together with global perturbations of the circuit (and is thus experimentally feasible). It promises to yield special insight into the hidden structure of the grid cell circuit. Finally, in Chapter 4, I provide an analytical treatment of pattern formation dynamics in the grid cell circuit. This work focuses on nonlinear effects.
Grid cell co-activity patterns remain stable across different behavioral states and experiences
(2017-12-11) Trettel, Sean Gregory; Colgin, Laura; Fiete, Ila; Johnston, Daniel; Aldrich, Rick; Mauk, Michael
Grid cells in the medial entorhinal cortex have been well studied while animals are exploring their environment; however, what they do when an animal is not navigating is less clear. Other cell types in the entorhinal-hippocampal network appear to have memory-related activity when an animal is inactive, so what grid cells do during quiescence is an important question. If grid cells show activity similar to place cells during rest and sleep, then it would imply that grid cells play an active role in memory functions rather than simply providing current sensory information to the hippocampus. Models have been proposed that make testable predictions about grid cell activity when spatial input is absent. The continuous attractor network model of grid cell pattern formation posits that grid cell patterning is a result of network connections between grid cells. As a result of this connectivity, these models hypothesize that grid cell co-activity patterns should be the same during sleep as during active navigation. In my first study, I investigated how spike time correlations between grid cell pairs during sleep compared to spike time correlations between the same grid cell pairs during waking activity. I found that the same correlation patterns were present regardless of whether spatial information was available to grid cells (i.e., during active navigation) or whether sensory input was absent (i.e., during sleep). These results support the continuous attractor network model hypothesis. In my second study, I examined whether novel experience changed grid cell co-activity patterns during active waking behaviors, rest, and sleep. I found that spike time correlations between grid cell pairs remained stable across behavioral states regardless of experience. In my last study, I looked at organized sequences of firing in grid cell ensembles to examine whether small changes in correlations led to detectable changes in more complex ensemble representations of experience. I found that grid cell ensemble activity did not appear to be influenced by different behaviors or novel experience. Taken together, these results suggest that grid cells are part of a low-dimensional, continuous attractor network and that grid cell activity patterns during sleep reflect connections in the grid cell network rather than representing specific experiences.
Unraveling the dynamics and structure of grid cells as a spatial map in the brain
(2015-12) Yoon, Ki Jung; Fiete, Ila; Vishwanath, Sriram; Bovik, Alan; Stone, Peter; Pillow, Jonathan
Grid cells, defined by their strikingly periodic spatial responses in open fields, have spurred widespread theoretical interest, and numerous models have been proposed to explain how grids are formed, how they are differentiated from the others, and how they might use idiothetic (self motion) information to path integrate. This dissertation leverages unique grid cell data together with computational and mathematical approaches to unravel grid cell dynamics and structure during navigation in general. First, we analyze several extensive datasets of grid cells recorded in 2-dimensional (2D) environments under a number of experimental manipulations, and show that the multi-dimensional network activity of grid cells is embedded into a two-dimensional continuous attractor manifold. Second, we analyze grid cell responses on linear 1-dimensional (1D) tracks to extract an underlying 2D grid structure. Combining Fourier analytical methods and numerical refinements, we show that the system remains in the same dynamical regime during navigation in 2D and 1D environments. Finally, we introduce a state-space point process filter to track the temporal evolution of spatial tuning curves and examine the error accumulation of grid cell system. We show that we can accurately infer the drift of the internal estimate of positions subsumed in the grid cell system as a path integrator.