Path integration and the neural basis of the 'cognitive map'
By Bruce L. McNaughton, Francesco Battaglia, Ole Jensen, Edvard I. Moser, May-Britt Moser. Nature Reviews Neuroscience, 2006
2,003 citations recorded by the source corpus.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/946e75e6548017341fde96e1008b9538bf5a96f9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
- James J. Knierim
- E. T. Rolls
- Joshua P. Neunuebel
- Neil Burgess
- Edvard I. Moser
- György Buzsáki
- May-Britt Moser
- Ila Rani Fiete
In Cognitive Neuroscience
- E. T. Rolls
- Neil Burgess
- James J. Knierim
- Joshua P. Neunuebel
- Edvard I. Moser
- György Buzsáki
- May-Britt Moser
- Matthew Botvinick
Citations in the corpus
- CA3 Retrieves Coherent Representations from Degraded Input: Direct Evidence for CA3 Pattern Completion and Dentate Gyrus Pattern Separation (2014, background): “…et al., 2005), it was assumed by many investigators that grid cells were the primary spatial drive onto place cells (e.g., Hafting et al., 2005; McNaughton et al., 2006; Monaco and Abbott, 2011; Savelli and Knierim, 2010; Solstad et al., 2006; but see Burgess et al., 2007; Kropff and Treves,…”
- The head direction signal: origins and sensory-motor integration. (2007, background): “The recent excitement of finding grid cells in the entorhinal cortex demonstrates this area’s potential importance for path integration (McNaughton et al. 2006), and recent models have shown how these cells may play a role in path integration (Fuhs & Touretzky 2006; compare Burak & Fiete 2006).”
- Accurate Path Integration in Continuous Attractor Network Models of Grid Cells (2008, background): “For instance, the proposal by [4] for wiring an atopographic and periodic network is based on three assumptions: (1) that another area, the ‘teacher’, contains an initial aperiodic, topographic network with population grid patterning and no velocity shift mechanism, (2) that the network pattern, whe”
- Evidence for grid cells in a human memory network (2010, methodology): “To test for the specificity of the six-fold symmetric sinusoidal modulation, we used the same approach of (1) estimating the mean grid orientation of entorhinal cortex and (2) looking for sinusoidal modulation in control models, however with directional periodicities of 90u and 45u (that is, four-fo”
- Molecularly Defined Circuitry Reveals Input-Output Segregation in Deep Layers of the Medial Entorhinal Cortex (2015, background): “…of spatial cognition and memory, as the high density of grid and border cells in superficial layers of the MEC is believed to be critical for path integration-based estimation of location (Hafting et al., 2005; McNaughton et al., 2006; Moser et al., 2008; Sargolini et al., 2006; Tang et al., 2014).”
- The neuroscience of learning: beyond the Hebbian synapse. (2013, background): “Dead reckoning plays an important role in map construction (Biegler 2000; Clark & Taube 2009; Collett & Collett 2009a,b; Gallistel 1990; McNaughton et al. 2006; Tcheang et al. 2011).”
- Learning Where to Attend with Deep Architectures for Image Tracking (2011, positive): “We make no attempt to implement such states with neural architectures, but it seems clear that they could be encoded with grid cells (McNaughton et al., 2006) and retinotopic maps as in V1 and the superior colliculus (Rosa, 2002; Gi-rard & Berthoz, 2005).”
- Using Grid Cells for Navigation (2015, background): “Interestingly, grid cells are widely believed to provide a path integration input to place cells, updating the representation of self-location by a vector describing the animal’s recent motion (Hafting et al., 2005; O’Keefe and Burgess, 2005; McNaughton et al., 2006; Rolls et al., 2006; Solstad et a”
- Remembering the past and imagining the future (2007, background): “…are supported by the recently discovered “grid cells” in the entorhinal cortex (Hafting et al., 2005), which appear well suited to this task, with the hippocampus potentially required when path integration has to be tied to environmental locations (O’Keefe & Burgess, 2005; McNaughton et al., 2006).”
- Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells (2007, background): “Therefore, many models combine local view and path integration mechanisms [41,42], but here we focus only on local view models.”