Sensory Feedback, Error Correction, and Remapping in a Multiple Oscillator Model of Place-Cell Activity

2012

Annu. Rev. Neurosci.

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184

163

Attractor networks are a popular computational construct used to model different brain systems. These networks allow elegant computations that are thought to represent a number of aspects of brain function. Although there is good reason to believe that the brain displays attractor dynamics, it has proven difficult to test experimentally whether any particular attractor architecture resides in any particular brain circuit. We review models and experimental evidence for three systems in the rat brain that are presumed to be components of the rat’s navigational and memory system. Head-direction cells have been modeled as a ring attractor, grid cells as a plane attractor, and place cells both as a plane attractor and as a point attractor. Whereas the models have proven to be extremely useful conceptual tools, the experimental evidence in their favor, although intriguing, is still mostly circumstantial.

Section: Grid Cells and Two-dimensional Continuous Attractorsmentioning

confidence: 99%

Attractor Dynamics of Spatially Correlated Neural Activity in the Limbic System

Knierim

2012

Annu. Rev. Neurosci.

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184

163

“…We must first let the system settle to a stable activity pattern, after which it performs exact path integration. The oscillatory interference models (15,16,20,27) of grid cells and place cells perform path integration by modulating the oscillator frequencies. Although the activity of the interfering oscillators is not spatially invariant, the envelope is.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, two leading classes of models of grid cells are continuous attractor networks (12-14) and oscillatory interference models (15)(16)(17)(18)(19)(20). These diverse models seemingly describe a diverse class of systems; however, deeper computational principles may exist that unify the different cases of neural integration.…”

mentioning

confidence: 99%

Universal conditions for exact path integration in neural systems

Issa

Proc. Natl. Acad. Sci. U.S.A.

2012

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Animals are capable of navigation even in the absence of prominent landmark cues. This behavioral demonstration of path integration is supported by the discovery of place cells and other neurons that show path-invariant response properties even in the dark. That is, under suitable conditions, the activity of these neurons depends primarily on the spatial location of the animal regardless of which trajectory it followed to reach that position. Although many models of path integration have been proposed, no known single theoretical framework can formally accommodate their diverse computational mechanisms. Here we derive a set of necessary and sufficient conditions for a general class of systems that performs exact path integration. These conditions include multiplicative modulation by velocity inputs and a path-invariance condition that limits the structure of connections in the underlying neural network. In particular, for a linear system to satisfy the pathinvariance condition, the effective synaptic weight matrices under different velocities must commute. Our theory subsumes several existing exact path integration models as special cases. We use entorhinal grid cells as an example to demonstrate that our framework can provide useful guidance for finding unexpected solutions to the path integration problem. This framework may help constrain future experimental and modeling studies pertaining to a broad class of neural integration systems.commutativity | attractor network | oscillatory interference | dead reckoning | Fourier analysis E ven without allothetic or environmental cues, animals are capable of finding their way home (1, 2), a process known as path integration or dead reckoning. This "integrative process" (3), whereby an internal representation of position is updated by incoming inertial or self-motion cues, was hypothesized over a century ago by Darwin (4) and Murphy (3). More recently, potential neural correlates of path integration have been discovered. For example, cells in brain regions associated with the Papez circuit can signal the heading direction of an animal (5, 6); grid cells in the entorhinal cortex presumably integrate this information and other self-motion cues to form a periodic spatial code (7-9); and, downstream in the hippocampus, place cells exhibit a sparser location code (10, 11).Many computational models of path integration have been proposed. For instance, two leading classes of models of grid cells are continuous attractor networks (12-14) and oscillatory interference models (15)(16)(17)(18)(19)(20). These diverse models seemingly describe a diverse class of systems; however, deeper computational principles may exist that unify the different cases of neural integration. Here we attempt to identify a general principle of path integration by starting with the exact requirement of invariance to movement trajectory in an arbitrary number of dimensions. This general approach allows us to derive a set of necessary and sufficient conditions for path integration and, for linear systems,...

“…http://dx.doi.org/10.1101/211458 doi: bioRxiv preprint first posted online Oct. 30, 2017; redundancy, and/or coupling with continuous attractors in the grid cell network (Zilli & Hasselmo,406 2010; Hasselmo & Brandon, 2012;Bush & Burgess, 2014 (Monaco et al, 2011). The phaser mechanism that we study here is a rate-to-phase 415 conversion that may provide the synchronous feedback signal needed for that calibration.…”

Section: Cc-by-nc-nd 40 International License Peer-reviewed) Is the mentioning

confidence: 99%

“…Thus the orthogonal subset will optimize competitive learning for phasers over repeated theta cycles in the same location; this subset evolves as the 449 animal explores the environment. Calibration in the familiar environment likewise requires 450 synchrony with phasers, but it must be interdigitated with path integration (Monaco et al, 2011) 451 perhaps mediated by discrete attentive behaviors during pauses in locomotion such as head 452 scanning (Monaco et al, 2014). Without this interplay, the spatial precision of the phase code …”

mentioning

confidence: 99%

Spatial Theta Cells in Competitive Burst Synchronization Networks: Reference Frames from Phase Codes

Monaco

Blair²,

2017

Preprint

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1Spatial cells of the hippocampal formation are embedded in networks of theta cells. The septal 2 theta rhythm (6-10 Hz) organizes the spatial activity of place and grid cells in time, but it remains 3 unclear how spatial reference points organize the temporal activity of theta cells in space. We 4 study spatial theta cells in simulations and single-unit recordings from exploring rats to ask 5 whether temporal phase codes may anchor spatial representations to the outside world. We 6 theorize that an experience-independent mechanism for temporal coding may combine with 7 burst synchronization to continuously calibrate self-motion to allocentric reference frames.