An oscillatory interference model of grid cell firing

By Neil Burgess, C. Barry, John O'Keefe. Hippocampus, 2007

702 citations recorded by the source corpus.

We expand upon our proposal that the oscillatory interference mechanism proposed for the phase precession effect in place cells underlies the grid‐like firing pattern of dorsomedial entorhinal grid cells (O'Keefe and Burgess ( 2005 ) Hippocampus 15:853–866). The original one‐dimensional interference model is generalized to an appropriate two‐dimensional mechanism. Specifically, dendritic subunits of layer II medial entorhinal stellate cells provide multiple linear interference patterns along different directions, with their product determining the firing of the cell. Connection of appropriate speed‐ and direction‐ dependent inputs onto dendritic subunits could result from an unsupervised learning rule which maximizes postsynaptic firing (e.g. competitive learning). These inputs cause the intrinsic oscillation of subunit membrane potential to increase above theta frequency by an amount proportional to the animal's speed of running in the “preferred” direction. The phase difference between this oscillation and a somatic input at theta‐frequency essentially integrates velocity so that the interference of the two oscillations reflects distance traveled in the preferred direction. The overall grid pattern is maintained in environmental location by phase reset of the grid cell by place cells receiving sensory input from the environment, and environmental boundaries in particular. We also outline possible variations on the basic model, including the generation of grid‐like firing via the interaction of multiple cells rather than via multiple dendritic subunits. Predictions of the interference model are given for the frequency composition of EEG power spectra and temporal autocorrelograms of grid cell firing as functions of the speed and direction of running and the novelty of the environment. © 2007 Wiley‐Liss, Inc.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/05de7373104d3098ce6850aae50c9dfd1da0a1a2

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Citations in the corpus

Theta oscillations provide temporal windows for local circuit computation in the entorhinal-hippocampal loop (2009, result): “This is in contrast to models based on the interference of global theta and single neuron oscillators (O’Keefe and Recce, 1993;Kamondi et al., 1998;Harris et al., 2002;Mehta et al., 2002;Lengyel et al., 2003;Burgess et al., 2007;Hasselmo, 2007,2008;Jeewajee et al., 2008).”
Active Inference: A Process Theory (2017, background): “This necessarily re-sults in a phase precession (Burgess et al., 2007; Lisman & Buzsaki, 2008; Lisman & Redish, 2009).”
Place cells, grid cells, and the brain's spatial representation system. (2008, background): “Burgess et al. (2007) extended the interference model to two dimensions by considering the interaction of one somatic intrinsic oscillator of frequency ws (∼ theta rhythm) with several dendritic oscillators, each with a frequency equal to ws plus a term proportional to the projection of the rat…”
CA3 Retrieves Coherent Representations from Degraded Input: Direct Evidence for CA3 Pattern Completion and Dentate Gyrus Pattern Separation (2014, background): “…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, 2008; O'Keefe and Burgess, 2005).”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, background): “In recent years, the dual oscillator interference model has been proposed to account for phase precession as well as the spatial pattern itself of grid cell activity (135, 136, 426, 737).”
Neural syntax: cell assemblies, synapsembles and readers (2010, unknown): “Since a similar temporal dynamic is at play in the entorhinal cortex (Burgess et al., 2007; Chrobak and Buzsáki, 1998; Hasselmo et al., 2009; Mizuseki et al., 2009; Moser et al., 2008), prefrontal cortex, and other structures (Benchenane et al., 2010; Berke et al., 2004; DeCoteau et al., 2007;…”
Accurate Path Integration in Continuous Attractor Network Models of Grid Cells (2008, background): “Armed with the proof-of-concept results that a continuous attractor network model can integrate velocity inputs accurately enough to produce SN grids, we next seek to explore testable predictions of the continuous attractor hypothesis in the grid cell system and contrast them with the properties of ”
Action understanding and active inference (2011, unknown): “It is interesting to think about the attractor states as representing trajectories through abstract representational spaces (cf., the activity of place cells; O'Keefe 1999; Tsodyks 1999; Burgess et al. 2007).”