Learning an Efficient Hippocampal Place Map from Entorhinal Inputs Using Non-Negative Sparse Coding

Lian, Yanbo; Burkitt, Anthony N.

doi:10.1523/eneuro.0557-20.2021

Cited by 15 publications

(23 citation statements)

References 66 publications

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“…Combining results in this paper and our previous work (Lian and Burkitt, 2021), the contribution of the EC to the firing of hippocampal place cells can be described as follows: both EC weakly spatial cells and cells with a clear structure (such as MEC grid cells) provide spatial information for the hippocampus to learn an efficient hippocampal place map, while the temporal response properties involving theta phase precession of hippocampal place cells are inherited from MEC grid cells via learning during navigation.…”

Section: Scenario 3: Spatial Properties Of Hippocampal Place Cells Ar...mentioning

confidence: 56%

“…Furthermore, after the learning process, these learnt hippocampal place cells still maintain their spatial selectivity even when MEC spatio-temporal grid cells are inactivated, suggesting that the remaining EC weakly spatial cells can maintain the place field responsiveness of place cells. Combining our previous work showing that either MEC grid cells or EC weakly spatial cells can give rise to the spatial tuning of hippocampal place cells (Lian and Burkitt, 2021), we complete the picture of how EC input contributes to the properties of hippocampal place cells from the perspective of synaptic plasticity; namely that hippocampal place cells learn the spatial properties from both MEC grid cells and EC weakly spatial cells, and learn their temporal properties from MEC grid cells. The synaptic plasticity underlying the learning model here is based on the principle of sparse coding.…”

Section: Introductionmentioning

confidence: 85%

“…Additional details about the implementation of non-negative sparse coding can be found in Lian and Burkitt (2021).…”

Section: Non-negative Sparse Codingmentioning

confidence: 99%

“…Consequently, MEC grid cells along with other cells in the EC are thought to provide spatial input for hippocampal place cells, so that they can have a specific place tuning. This has led to numerous feedforward EC-hippocampus models in which single place fields are generated from EC input, such as MEC grid cells or EC weakly spatial cells ( Rolls et al, 2006 ; Solstad et al, 2006 ; Franzius et al, 2007a , b ; de Almeida et al, 2009 ; Hasselmo, 2009 ; Savelli and Knierim, 2010 ; Neher et al, 2017 ; Lian and Burkitt, 2021 ). Furthermore, Fiete et al (2008) showed that this MEC grid cell structure represents an encoding scheme for position that is analogous to the residue number system ( Soderstrand et al, 1986 ), which is a highly efficient and accurate method for place representation ( Sreenivasan and Fiete, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

“…The learning model presented here is built on our previous work that shows that the model based on non-negative sparse coding can learn an efficient hippocampal place map using EC input such as MEC grid cells and EC weakly spatial cells ( Lian and Burkitt, 2021 ). In this paper, building on the work of Jeewajee et al (2014) and McClain et al (2019) , we construct a mathematical model of MEC spatiotemporal grid cells of a 2D environment.…”

Section: Introductionmentioning

confidence: 99%

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Learning Spatiotemporal Properties of Hippocampal Place Cells

Lian

Burkitt

2022

eNeuro

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It is well known that hippocampal place cells have spatio-temporal properties, namely that they generally respond to a single spatial location of a small environment, and they also display the temporal response property of theta phase precession, namely that the phase of spiking relative to the theta wave shifts from the late phase to early phase as the animal crosses the place field. Grid cells in layer II of the medial entorhinal cortex (MEC) also have spatio-temporal properties similar to hippocampal place cells, except that grid cells respond to multiple spatial locations that form a hexagonal pattern. Because the entorhinal cortex (EC) is the upstream area that projects strongly to the hippocampus, a number of EC-hippocampus learning models have been proposed to explain how the spatial receptive field properties of place cells emerge via synaptic plasticity. However, the question of how the phase precession properties of place cells and grid cells are related has remained unclear. This study shows how theta phase precession in hippocampal place cells can emerge from MEC input as a result of synaptic plasticity, demonstrating that a learning model based on non-negative sparse coding can account for both the spatial and temporal properties of hippocampal place cells. Although both MEC grid cells and other EC spatial cells contribute to the spatial properties of hippocampal place cells, it is the MEC grid cells that predominantly determine the temporal response properties of hippocampal place cells displayed here.

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Section: Scenario 3: Spatial Properties Of Hippocampal Place Cells Ar...mentioning

confidence: 56%

Section: Introductionmentioning

confidence: 85%

“…Additional details about the implementation of non-negative sparse coding can be found in Lian and Burkitt (2021).…”

Section: Non-negative Sparse Codingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning Spatiotemporal Properties of Hippocampal Place Cells

Lian

Burkitt

2022

eNeuro

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Gated transformations from egocentric to allocentric reference frames involving retrosplenial cortex, entorhinal cortex, and hippocampus

et al. 2023

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This paper reviews the recent experimental finding that neurons in behaving rodents show egocentric coding of the environment in a number of structures associated with the hippocampus. Many animals generating behavior on the basis of sensory input must deal with the transformation of coordinates from the egocentric position of sensory input relative to the animal, into an allocentric framework concerning the position of multiple goals and objects relative to each other in the environment. Neurons in retrosplenial cortex show egocentric coding of the position of boundaries in relation to an animal. These neuronal responses are discussed in relation to existing models of the transformation from egocentric to allocentric coordinates using gain fields and a new model proposing transformations of phase coding that differ from current models. The same type of transformations could allow hierarchical representations of complex scenes. The responses in rodents are also discussed in comparison to work on coordinate transformations in humans and non-human primates.

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