Dataset Information

Probabilistic Learning by Rodent Grid Cells.

ABSTRACT: Mounting evidence shows mammalian brains are probabilistic computers, but the specific cells involved remain elusive. Parallel research suggests that grid cells of the mammalian hippocampal formation are fundamental to spatial cognition but their diverse response properties still defy explanation. No plausible model exists which explains stable grids in darkness for twenty minutes or longer, despite being one of the first results ever published on grid cells. Similarly, no current explanation can tie together grid fragmentation and grid rescaling, which show very different forms of flexibility in grid responses when the environment is varied. Other properties such as attractor dynamics and grid anisotropy seem to be at odds with one another unless additional properties are assumed such as a varying velocity gain. Modelling efforts have largely ignored the breadth of response patterns, while also failing to account for the disastrous effects of sensory noise during spatial learning and recall, especially in darkness. Here, published electrophysiological evidence from a range of experiments are reinterpreted using a novel probabilistic learning model, which shows that grid cell responses are accurately predicted by a probabilistic learning process. Diverse response properties of probabilistic grid cells are statistically indistinguishable from rat grid cells across key manipulations. A simple coherent set of probabilistic computations explains stable grid fields in darkness, partial grid rescaling in resized arenas, low-dimensional attractor grid cell dynamics, and grid fragmentation in hairpin mazes. The same computations also reconcile oscillatory dynamics at the single cell level with attractor dynamics at the cell ensemble level. Additionally, a clear functional role for boundary cells is proposed for spatial learning. These findings provide a parsimonious and unified explanation of grid cell function, and implicate grid cells as an accessible neuronal population readout of a set of probabilistic spatial computations.

SUBMITTER: Cheung A

PROVIDER: S-EPMC5085080 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Probabilistic Learning by Rodent Grid Cells.

Cheung Allen A

PLoS computational biology 20161028 10

Mounting evidence shows mammalian brains are probabilistic computers, but the specific cells involved remain elusive. Parallel research suggests that grid cells of the mammalian hippocampal formation are fundamental to spatial cognition but their diverse response properties still defy explanation. No plausible model exists which explains stable grids in darkness for twenty minutes or longer, despite being one of the first results ever published on grid cells. Similarly, no current explanation ca ...[more]

PMID: 27792723

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Project description:BackgroundThe vision of learning healthcare systems (LHSs) is attractive as a more effective model for health care services, but achieving the vision is complex. There is limited literature describing the processes needed to construct such multicomponent systems or to assess development.MethodsWe used the concept of a capability maturity matrix to describe the maturation of necessary infrastructure and processes to create learning networks (LNs), multisite collaborative LHSs that use an actor-oriented network organizational architecture. We developed a network maturity grid (NMG) assessment tool by incorporating information from literature review, content theory from existing networks, and expert opinion to establish domains and components. We refined the maturity grid in response to feedback from network leadership teams. We followed NMG scores over time for nine LNs and plotted scores for each domain component with respect to SD for one participating network. We sought subjective feedback on the experience of applying the NMG to individual networks.ResultsLN leaders evaluated the scope, depth, and applicability of the NMG to their networks. Qualitative feedback from network leaders indicated that changes in NMG scores over time aligned with leaders' reports about growth in specific domains; changes in scores were consistent with network efforts to improve in various areas. Scores over time showed differences in maturation in the individual domains of each network. Scoring patterns, and SD for domain component scores, indicated consistency among LN leaders in some but not all aspects of network maturity. A case example from a participating network highlighted the value of the NMG in prompting strategic discussions about network development and demonstrated that the process of using the tool was itself valuable.ConclusionsThe capability maturity grid proposed here provides a framework to help those interested in creating Learning Health Networks plan and develop them over time.

Dataset Information

Probabilistic Learning by Rodent Grid Cells.

Publications

Probabilistic Learning by Rodent Grid Cells.

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