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Linear Look-Ahead in Conjunctive Cells: An Entorhinal Mechanism for Vector-Based Navigation

Overview of attention for article published in Frontiers in Neural Circuits, January 2012
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Title
Linear Look-Ahead in Conjunctive Cells: An Entorhinal Mechanism for Vector-Based Navigation
Published in
Frontiers in Neural Circuits, January 2012
DOI 10.3389/fncir.2012.00020
Pubmed ID
Authors

John L. Kubie, André A. Fenton

Abstract

The crisp organization of the "firing bumps" of entorhinal grid cells and conjunctive cells leads to the notion that the entorhinal cortex may compute linear navigation routes. Specifically, we propose a process, termed "linear look-ahead," by which a stationary animal could compute a series of locations in the direction it is facing. We speculate that this computation could be achieved through learned patterns of connection strengths among entorhinal neurons. This paper has three sections. First, we describe the minimal grid cell properties that will be built into our network. Specifically, the network relies on "rigid modules" of neurons, where all members have identical grid scale and orientation, but differ in spatial phase. Additionally, these neurons must be densely interconnected with synapses that are modifiable early in the animal's life. Second, we investigate whether plasticity during short bouts of locomotion could induce patterns of connections amongst grid cells or conjunctive cells. Finally, we run a simulation to test whether the learned connection patterns can exhibit linear look-ahead. Our results are straightforward. A simulated 30-min walk produces weak strengthening of synapses between grid cells that do not support linear look-ahead. Similar training in a conjunctive cell module produces a small subset of very strong connections between cells. These strong pairs have three properties: the pre- and post-synaptic cells have similar heading direction. The cell pairs have neighboring grid bumps. Finally, the spatial offset of firing bumps of the cell pair is in the direction of the common heading preference. Such a module can produce strong and accurate linear look-ahead starting in any location and extending in any direction. We speculate that this process may: (1) compute linear paths to goals; (2) update grid cell firing during navigation; and (3) stabilize the rigid modules of grid cells and conjunctive cells.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
France 1 1%
Netherlands 1 1%
Iran, Islamic Republic of 1 1%
United Kingdom 1 1%
Unknown 93 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 29%
Researcher 27 27%
Student > Master 10 10%
Student > Bachelor 6 6%
Student > Doctoral Student 5 5%
Other 5 5%
Unknown 18 18%
Readers by discipline Count As %
Neuroscience 28 28%
Agricultural and Biological Sciences 18 18%
Computer Science 10 10%
Psychology 8 8%
Engineering 7 7%
Other 9 9%
Unknown 20 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 April 2022.
All research outputs
#14,356,775
of 24,127,822 outputs
Outputs from Frontiers in Neural Circuits
#605
of 1,265 outputs
Outputs of similar age
#154,208
of 251,071 outputs
Outputs of similar age from Frontiers in Neural Circuits
#20
of 73 outputs
Altmetric has tracked 24,127,822 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,265 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 251,071 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.