A Gray Code model for the encoding of grid cells in the Entorhinal Cortex

Monteiro, Jânio; Pedro, André; Silva, A.

doi:10.1007/s00521-021-06482-w

Cited by 8 publications

(11 citation statements)

References 58 publications

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“…On the other hand, the Triangular Coordinate System [71], can handle certain amounts of noise in the data using simple mathematical operations, without using any special equipment like the neuromorphic devices. Yet, this model does not address a codification for 3D environments and the hexagons seem to a have a perfect hexagonal shape, which contrasts with what is observed in some neurobiological studies.…”

Section: Discussionmentioning

confidence: 99%

“…The information collected from the cones is aggregated with the data from the rods using the Horizontal Cells, which are inhibitory interneurons, that balance the overall activity of the retina and improve the contrast of the visual signal that is sent to the brain. According with [79], it is possible that Horizontal Cells can have some The Grid Cells main characteristics adapted from [3,5,71]. The left image shows the hexagonal shape of the Grid Cells, the right enhances the possible triangle properties.…”

Section: Visual Cortexmentioning

confidence: 99%

“…The left image shows the hexagonal shape of the Grid Cells, the right enhances the possible triangle properties. The bottom image is an adaptation of the obtained scientific results from [71].…”

Section: Visual Cortexmentioning

confidence: 99%

“…According to [71], it is possible to recreate the EC Grid Cells using simple mathematical properties. By separating the hexagonal shape into triangles and with a specific type of Gray code, the Grid Cells are formed and characterized using a Triangular Coordinate System.…”

Section: Entorhinal Cortex Modelsmentioning

confidence: 99%

“…Figure 3.The Grid Cells main characteristics adapted from[3,5,71]. The left image shows the hexagonal shape of the Grid Cells, the right enhances the possible triangle properties.…”

mentioning

confidence: 99%

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Perspective Chapter: Role of the Hippocampal Formation in Navigation from a Simultaneous Location and Mapping Perspective

Pedro¹,

Monteiro²,

Silva³

2023

Hippocampus - More Than Just Memory

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The research of the brain has led to many questions, with most of them still not having a definitive answer. One of those questions is about how the brain acts when we navigate a new space. Inside the Temporal Lobe’s Hippocampal structure, specific types of neurons and neuronal structures are responsible to identify spatial elements. To recognize spaces, these cells require data, which is obtained from the subject’s senses. It is important to understand how these features are captured, processed, encoded and how the Hippocampus, and its neighboring elements, use the information to help in the navigation and mapping of a place. A specific type of neurons seems to support an animals location and spatial mapping, on other areas of research, discrete global grid systems are used to increase the independence of the autonomous vehicles, allowing the indexing of assets across the globe by partitioning the earth into grids that take into account the heterogeneity of the scales of the associated geospatial data. In this context, the main objective of this chapter is to make an analysis about the biological and technical aspects of navigation by establishing a bridge between the Hippocampus and Simultaneous Localization and Mapping (SLAM) methods.

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Section: Discussionmentioning

confidence: 99%

Section: Visual Cortexmentioning

confidence: 99%

Section: Visual Cortexmentioning

confidence: 99%

Section: Entorhinal Cortex Modelsmentioning

confidence: 99%

“…Figure 3.The Grid Cells main characteristics adapted from[3,5,71]. The left image shows the hexagonal shape of the Grid Cells, the right enhances the possible triangle properties.…”

mentioning

confidence: 99%

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Perspective Chapter: Role of the Hippocampal Formation in Navigation from a Simultaneous Location and Mapping Perspective

Pedro¹,

Monteiro²,

Silva³

2023

Hippocampus - More Than Just Memory

Self Cite

View full text Add to dashboard Cite

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Improving a cortical pyramidal neuron model’s classification performance on a real-world ecg dataset by extending inputs

et al. 2023

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Pyramidal cells are the most prevalent neuronal type in the cortex, receiving thousands of synaptic inputs from all over the brain, and sending the largest axon outputs. They have a variety of active conductivities and complex morphologies that support highly nonlinear dendritic calculations. There has been a growing interest in understanding the classification abilities of pyramidal neurons. The perceptron learning algorithm, one of the foundations of machine learning, uses the highly simplified mathematical abstraction of a neuron, and it is unclear to what extent real biophysical neurons can perform perceptron like learning. In this article, we investigated the performance of a pyramidal neuron model in the classification problem of a two-class ECG dataset for different synaptic regions by using the perceptron learning method. The main purpose of this study is to reveal what role the soma, basilar and apical dendrites play in a classification problem. We concluded that when the synaptic receptor locations are selected close to the soma, classification performance close to Perceptron Learning in a Cortical Pyramidal Neuron Model the single layer perceptron can be obtained. The results indicated that the pyramidal neuron can successfully classify real-world data.

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Intentional and Undesirable Effects Related to the Interaction of Electromagnetic Fields with Major Living Tissues

Razek

2024

Preprint

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The well-being and sociability of individuals have always been part of modernity. The development of new technologies that meet these aspirations is receiving increasing attention. Thus, strengthening the desired objectives of these tools and minimizing their undesirable side effects is the subject of a growing commitment. The present contribution aims, in this context, to evaluate and analyze the desired and undesirable effects of the interaction of electromagnetic fields with living tissues in general. This involves routines reinforcing expected functions as well as those controlling and protecting against undesirable effects. First, the interactions of electromagnetic fields with tissues are analyzed, involving their thermal biological effects of desired and undesired exposures. The roles of blood and sap fluids in bio-affected tissues are then analyzed. Secondly, the governing electromagnetic, bio-heat equations, and their coupled solution are studied. Third, the thermal behavior of tissues and the exposure adverse effects are examined. Next, monitoring and defending of the effects of exposures are discussed. This contribution illustrates and discusses mathematical modeling routines based on a generalized interaction of electromagnetic fields with living tissues.

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A Gray Code model for the encoding of grid cells in the Entorhinal Cortex

Cited by 8 publications

References 58 publications

Perspective Chapter: Role of the Hippocampal Formation in Navigation from a Simultaneous Location and Mapping Perspective

Perspective Chapter: Role of the Hippocampal Formation in Navigation from a Simultaneous Location and Mapping Perspective

Improving a cortical pyramidal neuron model’s classification performance on a real-world ecg dataset by extending inputs

Intentional and Undesirable Effects Related to the Interaction of Electromagnetic Fields with Major Living Tissues

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