The motor influence score was defined as MI?=?( 1). Motor influence score for populace vectors To determine the engine influence score for populace vectors, for each session containing more than one cell, we computed one populace vector. place and grid cell firing patterns differentially reflect environmental info (or claims) and physical self-motion (or transitions), and need not become mutually coherent. Intro The spatial firing patterns of place cells and grid cells provide a windows into how we represent environmental location1,2 and potentially how we organise conceptual knowledge3,4. However, it is not obvious how these spatial representations are created. Place and grid cells might represent different sources of spatial info provided by the sensory environment and by self-motion5C7, or they might form a single coherent representation in which either place or grid cell firing is definitely strongly influenced from the additional cell type8C10. The unitary firing fields of place cells, their inclination to remap between environments with different sensory attributes11 and to switch parametrically following environmental changes12 indicate a strong influence of environmental info on place cell firing. By contrast, the regular periodic firing patterns of grid cells, taken care of across different environments, indicate a strong intrinsic organisation thought to be powered by self-motion inputs2,5C7. However, place cell firing patterns are affected by self-motion13, and grid cell firing patterns by environmental sensory inputs2,14C16. Crucially, the relative influence of self-motion and environmental sensory inputs within the firing of place and grid cells within a given animal has not been quantified, and we do not know whether the two cell types integrate these inputs separately, or combine?them to provide a single holistic representation. Normally, self-motion drives related changes in environmental inputs, so the two cannot Laniquidar be dissociated. However, virtual reality (VR) can be used to manipulate the relationship between physical (motoric/proprioceptive) self-motion signals and environmental visual info (including both identifiable landmarks and optic circulation) so that their relative influences can be identified. This approach has been used on 1-dimentional (1-d) virtual tracks while recording from place cells17 or grid cells18, suggesting that both types of input can Laniquidar influence the pattern of firing along the track in both types of cells, in ways that vary across cells17 and conditions18, see Discussion. Here we decoupled the physical self-motion and environmental visual signals available to mice operating in 2-d virtual open field environments, while recording from place and grid cells. We then compared the relative influences of these two types of info within the scales of the characteristic 2-d spatial firing patterns of place and grid cells. We used a VR system for mice, following a related system for rats19,20, which allows navigation and manifestation of spatial firing patterns within 2-d open field virtual environments21. Within the VR system, the effects of operating on a Styrofoam ball are used to drive movement of the viewpoint of the visual projection of the environment. In the baseline construction, Laniquidar movement of 1 1 unit of range on the surface of the ball is definitely translated to 1 1 unit of movement of the viewpoint within the virtual environment: the Rabbit Polyclonal to Myb gain between vision and movement is 1. Changes to this gain allow variations between the range indicated from the visual movement of viewpoint and the physical movement of the body. Under improved gain ratios (axis), so that the remaining (unchanged) dimensions provides a within-trial control for assessment and to determine any potentially confounding (non-spatial) effects, such as surprise or uncertainty. Finally, the use of VR removes potentially confounding local cues to location, whilst slightly reducing the overall Laniquidar strength of spatial coding21. In summary, place cell firing patterns mainly reflect visual inputs, while grid patterns reflect a much higher.