The medial temporal lobe (MTL) plays an important role in memory. The study discussed in this article examines how cell activity patterns transform visual information into long-term memory memories such as faces. The question of how this happens has plagued neuroscientists for decades. Evidence comes from electron physiology and lesion studies in monkeys. This shows that there is a hierarchical organization along the ventral visual pathway from the visual cortex V1 to the inferior temporal cortex (IT). Visual stimuli come in through this way and are processed and stored. How this information is exactly presented in there, remains a mystery.
Hypotheses
Two ideas for this representation have been drawn up based on two visions:
Idea 1: The "distribution population coding" vision states that a stimulus is represented by the activity of a large number of neuron combinations. Each neuron stands for a certain figure.
Idea 2: The “sparse coding” vision states that a perception is represented by smaller neuron combinations, of neurons that respond specifically to specific figures, objects and concepts. Because of this, neuron scientists thought that one neuron corresponds to an object or person, regardless of how this was observed. These cells were called grandmother cells. The question is whether these cells exist at all?
Neurons in IT and MTL
The IT sends information to the MTL. It is important to know that the MTL is not a homogeneous structure with a single function. Neurons in the MTL respond selectively when composing gender and facial expression on photos and in real life with both acquaintances and strangers. This combination of selectivity leads to an explicit representation in which a single cell can serve as an indicator to see which person is being depicted.
These cells resemble grandmother cells, but it is impossible that only one cell responds. In any case, it is even more unbelievable that you can find this cell. In addition, the cell may respond to several people, because in one study you cannot visit all the people in the world. It has been proven that some units fire at several people. Finally, theoretical findings estimate that each unit must have 50-150 cells to distinguish between people.
It is important to investigate whether these abstract cells can also be found in animal species. In rats, these abstract cells would be expected to be located in the neocortical areas for object recognition, such as the IT cortex. However, it is very difficult to detect such a group of firing neurons.
Research with learning tasks showed that after an unsupervised learning task, the previously random-firing neuron units became units that simultaneously fire at one object from the learning task. In this way this object or individual would be recognized. Most of these units respond uniquely to a single individual.
The findings of neurological patients showed that the MTL is not necessary for visual recognition. The hippocampal rhinal system is involved in long-term declarative memory. The MTL cells, described in the previous sections, connect visual perception to memory. So they do not really provide recognition. Recognition happens after about 130 ms, while the units (from the previous section) only started firing at a certain stimulus between 250-350 ms.
If the MTL is involved in memory, it is likely that these neurons do not know all the differences within visual details. The existence of category cells such as the units (which respond to individuals) corresponds to the vision that they encode aspects of the meaning we have of a stimulus that we want to remember. These cells are probably also involved in learning associations. These units are therefore not grandmother cells.
Summaries per article with Artificial intelligences and Neurocognition at Leiden University 20/21
Summaries per article with Artificial intelligences and Neurocognition at Leiden University 20/21
Study Guide with article summaries for Artificial Intelligence at Leiden University
Article summaries with Artificial Intelligence at Leiden University
Table of content
- Modeling visual recognition from neurobiological constraints
- Achieving machine realization of truly human-like intelligence
- Untangling invariant object recognition
- Computing machinery and intelligence
- Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project
- Robots with instincts
- Male and female robots
- Speed of processing in the human visual system
- Sparse but not ""Grandmother-cell"" coding in the medial temporal lobe
- Perceptrons
- Learning and neural plasticity in visual object recognition
- Breaking position-invariant object recognition
- A feedforward architecture accounts for rapid categorization
- Hierarchical models of object recognition in cortex
- Articlesummaries with prescribed articles for AI - 2020/2021
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