Vector Trace cells in the Subiculum of the Hippocampal formation

Poulter, Steven; Lee, Sang Ah; Dachtler, James; Wills, Thomas J.; Lever, Colin

doi:10.1101/805242

Cited by 7 publications

(10 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…80 ). Vectorial trace responses have recently been reported in the rodent subiculum 81 . These cells fire in response to environmental features at a certain allocentric distance and direction, including extended boundaries and smaller objects (i.e., similar to boundary vector cells in theoretical models).…”

Section: Fig 5a Depicts a High-level Schematic Of This Theoretical Fmentioning

confidence: 97%

“…5b-c) 21 . These cells have been termed 'trace vector cells' 81 , and they appear to be distinct from non-trace vector cells, being found in the distal and proximal subiculum, respectively. This may suggest that distinct sub-populations of vectorial neurons code for perceptual (non-trace cells) versus mnemonic aspects (vector trace cells) of scene representation (FIG.…”

Section: Fig 5a Depicts a High-level Schematic Of This Theoretical Fmentioning

confidence: 99%

See 1 more Smart Citation

Neuronal vector coding in spatial cognition

2020

View full text Add to dashboard Cite

Several types of neurons involved in spatial navigation and memory encode the distance and direction (that is, the vector) between an agent and items in its environment. Such vectorial information provides a powerful basis for spatial cognition by representing the geometric relationships between the self and the external world. Here, we review the explicit encoding of vectorial information by neurons in and around the hippocampal formation, far from the sensory periphery. The parahippocampal, retrosplenial and parietal cortices, as well as the hippocampal formation and striatum, provide a plethora of examples of vector coding at the single neuron level. We provide a functional taxonomy of cells with vectorial receptive fields as reported in experiments and proposed in theoretical work. The responses of these neurons may provide the fundamental neural basis for the (bottom-up) representation of environmental layout and (top-down) memoryguided generation of visuo-spatial imagery and navigational planning. [H1] Introduction Place cells fire whenever an animal traverses a specific location in an environment (the spatial receptive field [G] of that neuron, also known as its 'place field', FIG. 1a). Since the discovery of place cells in the rat hippocampus by O'Keefe and Dostrovsky 1 , researchers have uncovered a multitude of additional spatially-selective cell types in rodents: that is, neurons whose receptive fields reference some aspect of an organism's location, state of motion, pose or its relationship to environmental features (such as boundaries, landmarks and other objects). The spatial receptive fields of some of these cells correspond to vectors, indicating the distance and direction in space (relative to the animal's current location) within which the presence of an environmental feature will drive the neuron to fire. Such 'vectorial codes' for space have received comparatively less attention than the coding performed by place cells or other well-known spatially-selective cell types, such as grid cells [G] 2 (FIG. 1a) and head direction cells [G] 3,4. Vectorial codes for space are expressed by boundary vector cells 5,6 , border cells 7,8 , landmark vector cells 9 and object vector cells 10 in allocentric (that is, world-centered) coordinates. Just like place fields, the receptive fields of these vector-coding cells do not reflect the orientation of the animal but do rotate together with the prominent environmental features that control head direction cell firing 11. Egocentric counterparts of some of these cells-in which the direction of receptive fields are referenced relative to the facing direction of the agent-have also been found 12-15 , as well as cells that exhibit head direction-modulated boundary responses 16,17 .

show abstract

Section: Fig 5a Depicts a High-level Schematic Of This Theoretical Fmentioning

confidence: 97%

Section: Fig 5a Depicts a High-level Schematic Of This Theoretical Fmentioning

confidence: 99%

Neuronal vector coding in spatial cognition

2020

View full text Add to dashboard Cite

show abstract

“…A full understanding of the spatial circuits in the mammalian brain will require resolving the functional dependence between different representations. In our model the relationship between the HD and BVC systems is determined by their joint association to visual memories (Extended Data Fig 10) - thus they rotate coherently to follow changes in the angular location of a familiar distal cue 35 (Extended Data Fig 5). Conversely, in different enclosures with non-overlapping sets of visual memories, groups of BVCs may rotate and flip relative to the HD-system while retaining their internal coherence 5 .…”

Section: Introductionmentioning

confidence: 99%

A model of egocentric to allocentric understanding in mammalian brains

Uría

Ibarz

Banino

et al. 2020

Preprint

View full text Add to dashboard Cite

In the mammalian brain, allocentric representations support efficient self-location and flexible navigation. A number of distinct populations of these spatial responses have been identified but no unified function has been shown to account for their emergence. Here we developed a network, trained with a simple predictive objective, that was capable of mapping egocentric information into an allocentric spatial reference frame. The prediction of visual inputs was sufficient to drive the appearance of spatial representations resembling those observed in rodents: head direction, boundary vector, and place cells, along with the recently discovered egocentric boundary cells, suggesting predictive coding as a principle for their emergence in animals. The network learned a solution for head direction tracking convergent with known biological connectivity, while suggesting a possible mechanism of boundary cell remapping. Moreover, like mammalian representations, responses were robust to environmental manipulations, including exposure to novel settings, and could be replayed in the absence of perceptual input, providing the means for offline learning. In contrast to existing reinforcement learning approaches, agents equipped with this network were able to flexibly reuse learnt behaviours - adapting rapidly to unfamiliar environments. Thus, our results indicate that these representations, derived from a simple egocentric predictive framework, form an efficient basis-set for cognitive mapping.

show abstract

“…This is of interest in the context of spatiotemporal coding, as recent modelling work utilised periodic modulation of top-down connection weights, akin to the theta oscillation, to enable an agent to make comparisons between perceived and memorised scenes (Bicanki and Burgess, 2018) similar to the proposal of theta phase separation for encoding and retrieval in the hippocampus (Hasselmo et al, 2002). Consistent with this, allocentric boundary cells in the subiculum fire on different phases of theta during direct experience of boundaries versus the phase of firing for trace responses to boundaries that are no longer present (Poulter et al, 2019). Egocentric sensory information must be temporally synchronised with movement through space and time to form contextually rich episodic memories.…”

Section: Egocentric Boundary Cellsmentioning

confidence: 96%

Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex

Alexander

Robinson

Dannenberg

et al. 2020

Brain and Neuroscience Advances

View full text Add to dashboard Cite

Neurophysiological recordings in behaving rodents demonstrate neuronal response properties that may code space and time for episodic memory and goal-directed behaviour. Here, we review recordings from hippocampus, entorhinal cortex, and retrosplenial cortex to address the problem of how neurons encode multiple overlapping spatiotemporal trajectories and disambiguate these for accurate memory-guided behaviour. The solution could involve neurons in the entorhinal cortex and hippocampus that show mixed selectivity, coding both time and location. Some grid cells and place cells that code space also respond selectively as time cells, allowing differentiation of time intervals when a rat runs in the same location during a delay period. Cells in these regions also develop new representations that differentially code the context of prior or future behaviour allowing disambiguation of overlapping trajectories. Spiking activity is also modulated by running speed and head direction, supporting the coding of episodic memory not as a series of snapshots but as a trajectory that can also be distinguished on the basis of speed and direction. Recent data also address the mechanisms by which sensory input could distinguish different spatial locations. Changes in firing rate reflect running speed on long but not short time intervals, and few cells code movement direction, arguing against path integration for coding location. Instead, new evidence for neural coding of environmental boundaries in egocentric coordinates fits with a modelling framework in which egocentric coding of barriers combined with head direction generates distinct allocentric coding of location. The egocentric input can be used both for coding the location of spatiotemporal trajectories and for retrieving specific viewpoints of the environment. Overall, these different patterns of neural activity can be used for encoding and disambiguation of prior episodic spatiotemporal trajectories or for planning of future goal-directed spatiotemporal trajectories.

show abstract

Vector Trace cells in the Subiculum of the Hippocampal formation

Cited by 7 publications

References 48 publications

Neuronal vector coding in spatial cognition

Neuronal vector coding in spatial cognition

A model of egocentric to allocentric understanding in mammalian brains

Neurophysiological coding of space and time in the hippocampus, entorhinal cortex, and retrosplenial cortex

Contact Info

Product

Resources

About