scholarly journals A structured scaffold underlies activity in the hippocampus

2021 ◽  
Author(s):  
Dounia Mulders ◽  
Man Yi Yim ◽  
Jae Sung Lee ◽  
Albert K. Lee ◽  
Thibaud Taillefumier ◽  
...  
Keyword(s):  
Random Process ◽  
Grid Cell ◽  
Place Cell ◽  
Place Cells ◽  
Head Direction ◽  
Cell Responses ◽  
Neural Populations ◽  
External Cues ◽  
One Step ◽  
Design Experiments

Place cells are believed to organize memory across space and time, inspiring the idea of the cognitive map. Yet unlike the structured activity in the associated grid and head-direction cells, they remain an enigma: their responses have been difficult to predict and are complex enough to be statistically well-described by a random process. Here we report one step toward the ultimate goal of understanding place cells well enough to predict their fields. Within a theoretical framework in which place fields are derived as a conjunction of external cues with internal grid cell inputs, we predict that even apparently random place cell responses should reflect the structure of their grid inputs and that this structure can be unmasked if probed in sufficiently large neural populations and large environments. To test the theory, we design experiments in long, locally featureless spaces to demonstrate that structured scaffolds undergird place cell responses. Our findings, together with other theoretical and experimental results, suggest that place cells build memories of external inputs by attaching them to a largely prespecified grid scaffold.

scholarly journals Place-cell capacity and volatility with grid-like inputs

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Man Yi Yim ◽  
Lorenzo A Sadun ◽  
Ila R Fiete ◽  
Thibaud Taillefumier
Keyword(s):  
Grid Cell ◽  
Place Cell ◽  
Place Cells ◽  
Place Field ◽  
Cell Capacity ◽  
Spatial Range ◽  
External Cues ◽  
Noise Robust ◽  
Large Response

What factors constrain the arrangement of the multiple fields of a place cell? By modeling place cells as perceptrons that act on multiscale periodic grid-cell inputs, we analytically enumerate a place cell's repertoire - how many field arrangements it can realize without external cues while its grid inputs are unique; and derive its capacity - the spatial range over which it can achieve any field arrangement. We show that the repertoire is very large and relatively noise-robust. However, the repertoire is a vanishing fraction of all arrangements, while capacity scales only as the sum of the grid periods so field arrangements are constrained over larger distances. Thus, grid-driven place field arrangements define a large response scaffold that is strongly constrained by its structured inputs. Finally, we show that altering grid-place weights to generate an arbitrary new place field strongly affects existing arrangements, which could explain the volatility of the place code.

scholarly journals Where can a place cell put its fields? Let us count the ways

2019 ◽  
Author(s):  
Man Yi Yim ◽  
Lorenzo A Sadun ◽  
Ila R Fiete ◽  
Thibaud Taillefumier
Keyword(s):  
Grid Cell ◽  
Place Cell ◽  
Place Cells ◽  
Small Range ◽  
Place Field ◽  
Structured Grid ◽  
Cell Scaffold ◽  
External Cues ◽  
Relative Robustness ◽  
Hippocampal Place Cell

AbstractA hippocampal place cell exhibits multiple firing fields within and across environments. What factors determine the configuration of these fields, and could they be set down in arbitrary locations? We conceptualize place cells as performing evidence combination across many inputs and selecting a threshold to fire. Thus, mathematically they are perceptrons, except that they act on geometrically organized inputs in the form of multiscale periodic grid-cell drive, and external cues. We analytically count which field arrangements a place cell can realize with structured grid inputs, to show that many more place-field arrangements are realizable with grid-like than one-hot coded inputs. However, the arrangements have a rigid structure, defining an underlying response scaffold. We show that the “separating capacity” or spatial range over which all potential field arrangements are realizable equals the rank of the grid-like input matrix, which in turn equals the sum of distinct grid periods, a small fraction of the unique grid-cell coding range. Learning different grid-to-place weights beyond this small range will alter previous arrangements, which could explain the volatility of the place code. However, compared to random inputs over the same range, grid-structured inputs generate larger margins, conferring relative robustness to place fields when grid input weights are fixed.Significance statementPlace cells encode cognitive maps of the world by combining external cues with an internal coordinate scaffold, but our ability to predict basic properties of the code, including where a place cell will exhibit fields without external cues (the scaffold), remains weak. Here we geometrically characterize the place cell scaffold, assuming it is derived from multiperiodic modular grid cell inputs, and provide exact combinatorial results on the space of permitted field arrangements. We show that the modular inputs permit a large number of place field arrangements, with robust fields, but also strongly constrain their geometry and thus predict a structured place scaffold.

Hippocampal Place-Cell Firing During Movement in Three-Dimensional Space

2001 ◽  
Vol 85 (1) ◽  
pp. 105-116 ◽  
Author(s):  
James J. Knierim ◽  
Bruce L. McNaughton
Keyword(s):  
Hippocampal Neurons ◽  
Horizontal Plane ◽  
Three Dimensional ◽  
Place Cell ◽  
Place Cells ◽  
Limbic Cortex ◽  
Head Direction ◽  
Head Direction Cells ◽  
Recording Apparatus ◽  
Place Fields

“Place” cells of the rat hippocampus are coupled to “head direction” cells of the thalamus and limbic cortex. Head direction cells are sensitive to head direction in the horizontal plane only, which leads to the question of whether place cells similarly encode locations in the horizontal plane only, ignoring the z axis, or whether they encode locations in three dimensions. This question was addressed by recording from ensembles of CA1 pyramidal cells while rats traversed a rectangular track that could be tilted and rotated to different three-dimensional orientations. Cells were analyzed to determine whether their firing was bound to the external, three-dimensional cues of the environment, to the two-dimensional rectangular surface, or to some combination of these cues. Tilting the track 45° generally provoked a partial remapping of the rectangular surface in that some cells maintained their place fields, whereas other cells either gained new place fields, lost existing fields, or changed their firing locations arbitrarily. When the tilted track was rotated relative to the distal landmarks, most place fields remapped, but a number of cells maintained the same place field relative to the x-y coordinate frame of the laboratory, ignoring the z axis. No more cells were bound to the local reference frame of the recording apparatus than would be predicted by chance. The partial remapping demonstrated that the place cell system was sensitive to the three-dimensional manipulations of the recording apparatus. Nonetheless the results were not consistent with an explicit three-dimensional tuning of individual hippocampal neurons nor were they consistent with a model in which different sets of cells are tightly coupled to different sets of environmental cues. The results are most consistent with the statement that hippocampal neurons can change their “tuning functions” in arbitrary ways when features of the sensory input or behavioral context are altered. Understanding the rules that govern the remapping phenomenon holds promise for deciphering the neural circuitry underlying hippocampal function.

scholarly journals Intuitive planning: global navigation through cognitive maps based on grid-like codes

2018 ◽  
Author(s):  
Alon B. Baram ◽  
Timothy H. Muller ◽  
James C.R. Whittington ◽  
Timothy E.J. Behrens
Keyword(s):  
Spatial Information ◽  
Distance Measure ◽  
Grid Cell ◽  
Simple Algorithm ◽  
Place Cells ◽  
Grid Cells ◽  
Global Knowledge ◽  
The World ◽  
One Step ◽  
The Relationship

AbstractIt is proposed that a cognitive map encoding the relationships between objects supports the ability to flexibly navigate the world. Place cells and grid cells provide evidence for such a map in a spatial context. Emerging evidence suggests analogous cells code for non-spatial information. Further, it has been shown that grid cells resemble the eigenvectors of the relationship between place cells and can be learnt from local inputs. Here we show that these locally-learnt eigenvectors contain not only local information but also global knowledge that can provide both distributions over future states as well as a global distance measure encoding approximate distances between every object in the world. By simply changing the weights in the grid cell population, it is possible to switch between computing these different measures. We demonstrate a simple algorithm can use these measures to globally navigate arbitrary topologies without searching more than one step ahead. We refer to this as intuitive planning.

scholarly journals Learning place cells, grid cells and invariances: A unifying model

2017 ◽  
Author(s):  
Simon N. Weber ◽  
Henning Sprekeler
Keyword(s):  
Cell Types ◽  
Place Cell ◽  
Place Cells ◽  
Head Direction ◽  
Brain Areas ◽  
Neural Representations ◽  
Spatial Tuning ◽  
Spatial Dimensions ◽  
Inhibitory Plasticity ◽  
Adjacent Brain

AbstractNeurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction. The underlying circuit mechanisms are not fully resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, a place cell is highly selective to location, but typically invariant to head direction. Here, we propose that all observed spatial tuning patterns – in both their selectivity and their invariance – are a consequence of the same mechanism: Excitatory and inhibitory synaptic plasticity that is driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. The model is robust to changes in parameters, develops patterns on behavioral time scales and makes distinctive experimental predictions. Our results suggest that the interaction of excitatory and inhibitory plasticity is a general principle for the formation of neural representations.

scholarly journals On the Organization of Grid and Place Cells: Neural Denoising via Subspace Learning

2019 ◽  
Vol 31 (8) ◽  
pp. 1519-1550
Author(s):  
David M. Schwartz ◽  
O. Ozan Koyluoglu
Keyword(s):  
Hebbian Learning ◽  
Grid Cell ◽  
Subspace Learning ◽  
Place Cell ◽  
Place Cells ◽  
Neural Code ◽  
Neural Representation ◽  
Cell Populations ◽  
Medial Entorhinal Cortex ◽  
Cell Networks

Place cells in the hippocampus (HC) are active when an animal visits a certain location (referred to as a place field) within an environment. Grid cells in the medial entorhinal cortex (MEC) respond at multiple locations, with firing fields that form a periodic and hexagonal tiling of the environment. The joint activity of grid and place cell populations, as a function of location, forms a neural code for space. In this article, we develop an understanding of the relationships between coding theoretically relevant properties of the combined activity of these populations and how these properties limit the robustness of this representation to noise-induced interference. These relationships are revisited by measuring the performances of biologically realizable algorithms implemented by networks of place and grid cell populations, as well as constraint neurons, which perform denoising operations. Contributions of this work include the investigation of coding theoretic limitations of the mammalian neural code for location and how communication between grid and place cell networks may improve the accuracy of each population's representation. Simulations demonstrate that denoising mechanisms analyzed here can significantly improve the fidelity of this neural representation of space. Furthermore, patterns observed in connectivity of each population of simulated cells predict that anti-Hebbian learning drives decreases in inter-HC-MEC connectivity along the dorsoventral axis.

scholarly journals Towards a Predictive Bio-Inspired Navigation Model

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 100
Author(s):  
Simon Gay ◽  
Kévin Le Le Run ◽  
Edwige Pissaloux ◽  
Katerine Romeo ◽  
Christèle Lecomte
Keyword(s):  
Predictive Model ◽  
Large Scale ◽  
Environmental Changes ◽  
Grid Cell ◽  
Visual Navigation ◽  
Place Cells ◽  
Head Direction ◽  
Head Direction Cells ◽  
Visually Impaired People ◽  
Proposed Model

This paper presents a novel bio-inspired predictive model of visual navigation inspired by mammalian navigation. This model takes inspiration from specific types of neurons observed in the brain, namely place cells, grid cells and head direction cells. In the proposed model, place cells are structures that store and connect local representations of the explored environment, grid and head direction cells make predictions based on these representations to define the position of the agent in a place cell’s reference frame. This specific use of navigation cells has three advantages: First, the environment representations are stored by place cells and require only a few spatialized descriptors or elements, making this model suitable for the integration of large-scale environments (indoor and outdoor). Second, the grid cell modules act as an efficient visual and absolute odometry system. Finally, the model provides sequential spatial tracking that can integrate and track an agent in redundant environments or environments with very few or no distinctive cues, while being very robust to environmental changes. This paper focuses on the architecture formalization and the main elements and properties of this model. The model has been successfully validated on basic functions: mapping, guidance, homing, and finding shortcuts. The precision of the estimated position of the agent and the robustness to environmental changes during navigation were shown to be satisfactory. The proposed predictive model is intended to be used on autonomous platforms, but also to assist visually impaired people in their mobility.

scholarly journals Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations

2014 ◽  
Vol 369 (1635) ◽  
pp. 20120524 ◽  
Author(s):  
Stephen Grossberg ◽  
Praveen K. Pilly
Keyword(s):  
Entorhinal Cortex ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Grid Cell ◽  
Neural Model ◽  
Gamma Oscillations ◽  
Place Cell ◽  
Place Cells ◽  
Grid Cells ◽  
Map Learning

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC (‘neural relativity’). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.

scholarly journals Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Yedidyah Dordek ◽  
Daniel Soudry ◽  
Ron Meir ◽  
Dori Derdikman
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Hexagonal Lattice ◽  
Grid Cell ◽  
Principal Component ◽  
Component Analysis ◽  
Place Cell ◽  
Place Cells ◽  
Grid Cells ◽  
Spacing Ratio

Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA.

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