Learning spatio-temporal properties of hippocampal place cells

Lian, Yanbo; An, Burkitt

doi:10.1101/2021.07.13.452268

Cited by 3 publications

(2 citation statements)

References 66 publications

(91 reference statements)

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“…In this model, the V1-RSC network implements non-negative sparse coding. Similar to our previous work (Lian and Burkitt, 2021a,b), we implement the model via a locally competitive algorithm (Rozell et al, 2008) that efficiently solves sparse coding as follows: and where I is the input from V1 (i.e., complex cells responses), s represent the response (firing rate) of the neurons in the RSC, u can be interpreted as the corresponding membrane potential, A is the matrix containing basis vectors and can be interpreted as the connection weights between complex cells in V1 and neurons in the RSC, Y = A T A − 𝟙 and can be interpreted as the recurrent connection between neurons in the RSC, 𝟙 is the identity matrix, τ is the time constant of the RSC neurons, λ is the positive sparsity constant that controls the threshold of firing, and η is the learning rate. Each column of A is normalised to have length 1.…”

Section: Methodssupporting

confidence: 89%

“…Along with its variant, non-negative sparse coding (Hoyer, 2003), the principle of sparse coding provides a compelling explanation for neurophysiological findings for many brain areas such as the retina, visual cortex, auditory cortex, olfactory cortex, somatosensory cortex and other areas (see Beyeler et al (2019) for a review). Recently, sparse coding with non-negative constraint has been shown to provide an account for learning of the spatial and temporal properties of hippocampal place cells within the entorhinal-hippocampal network (Lian and Burkitt, 2021a,b).…”

Section: Methodsmentioning

confidence: 99%

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Learning the Vector Coding of Egocentric Boundary Cells from Visual Data

Lian

Williams

Alexander

et al. 2022

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The use of spatial maps to navigate through the world requires a complex ongoing transformation of egocentric views of the environment into position within the allocentric map. Recent research has discovered neurons in retrosplenial cortex and other structures that could mediate the transformation from egocentric views to allocentric views. These egocentric boundary cells (EBCs) respond to the egocentric direction and distance of barriers relative to an animals point of view. This egocentric coding based on the visual features of barriers would seem to require complex dynamics of cortical interactions. However, computational models presented here show that egocentric boundary cells can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Simulation of this simple sparse synaptic modification generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. This provides a framework for understanding the properties of neuronal populations in the retrosplenial cortex that may be essential for interfacing egocentric sensory information with allocentric spatial maps of the world formed by neurons in downstream areas including the grid cells in entorhinal cortex and place cells in the hippocampus.

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Section: Methodssupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Learning the Vector Coding of Egocentric Boundary Cells from Visual Data

Lian

Williams

Alexander

et al. 2022

Preprint

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Unifying sparse coding, predictive coding, and divisive normalization

Lian

Burkitt

2023

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Sparse coding, predictive coding and divisive normalization are thought to be underlying principles of neural circuits across the brain, supported by much experimental evidence. However, the connection among these principles is still poorly understood. In this paper, we aim to unify sparse coding, predictive coding, and divisive normalization in the context of a two-layer neural model. This two-layer model is constructed that implements sparse coding with the structure originating from predictive coding. Our results show that a homeostatic function in the model can shape the nonlinearity of neural responses, which can replicate different forms of divisive normalization. We demonstrate that this model is equivalent to divisive normalization in a single-neuron scenario. Our simulation also shows that the model can learn simple cells with the property of contrast saturation that is previously explained by divisive normalization. In summary, our study demonstrates that the three principles of sparse coding, predictive coding, and divisive normalization can be unified, such that the model has the ability to learn as well as display more diverse response nonlinearities. This framework may also be potentially used to explain how the brain learns to integrate input from different sensory modalities.

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Learning spatio-temporal V1 cells from diverse LGN inputs

Ruslim,

Burkitt,

Lian

2023

Preprint

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Since the Nobel-winning discovery of simple cells and complex cells in cat’s primary visual cortex (V1) by Hubel and Wiesel, many experimental studies of the visual system of the brain have been conducted. Experimental data of V1 cells from animal recordings show spatio-temporal properties, namely that they display both spatial and temporal response properties. For spatial properties, each V1 cell responds to a specific feature (such as bars, blobs, etc.) in the visual space, which is called the receptive field of this cell. The receptive fields of different V1 cells are typically different in size, orientation, spatial frequencies, etc. Furthermore, V1 cells also display temporal properties, namely that the receptive fields of V1 cells can change over time, and there is a great variety of ways in which they change over time. For example, the population of V1 cells show a great diversity from monophasic response to biphasic response, and some V1 cells are selective to a preferred direction. However, given many computational learning models that explain how spatial properties of V1 cells can be learnt, how temporal properties emerge is still not well understood. In this paper, we use a simple learning model based on sparse coding to show that spatio-temporal V1 cells, such as biphasic and direction selective cell, can emerge via synaptic plasticity when diverse spatio-temporal LGN cells are used as upstream input to V1 cells. This work suggests that temporal along with spatial properties of V1 cells may simply come from a learning process that aims to encode upstream input with spatio-temporal properties, which greatly enhances our understanding of V1 cells.

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Learning spatio-temporal properties of hippocampal place cells

Cited by 3 publications

References 66 publications

Learning the Vector Coding of Egocentric Boundary Cells from Visual Data

Learning the Vector Coding of Egocentric Boundary Cells from Visual Data

Unifying sparse coding, predictive coding, and divisive normalization

Learning spatio-temporal V1 cells from diverse LGN inputs

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