Researchers Come Up with a New Explanation of Processes that Underlie Working Memory
Researchers from the HSE Centre for Cognition & Decision Making have developed a computational model of working memory and demonstrated the stabilizing effect of gamma oscillations, as well as the importance of fast interaction between the model components. The study results have the potential to become part of a theoretical basis for experiments on improving working memory functions with non-invasive brain stimulation. The study was published in Frontiers in Neural Circuits.
Human memory has a complicated structure that allows the brain to store information at various timescales. When it is needed to perform actions based on information that is currently unavailable to sensory organs, the human brain relies on working (short-term) memory. We need it to perform reasoning, reflect, understand complex information, and make decisions.
The human brain constantly produces electrical activity. Neurons are the brain cells that exchange information using brief electric impulses (spikes). When information is retained in working memory, neurons in the prefrontal cortex are in the active state characterized by an increased firing rate (i.e. rate of spike generation). In addition to the spiking activity of individual neurons, working memory also relies on the collective rhythmic activity of brain neural networks in different frequency bands.
Among the various types of brain rhythmic activity that are observed during working memory retention, gamma-band oscillations seem particularly interesting. The gamma band spans the frequencies of brain oscillations from 40 to 170 Hz. Gamma activity indicates the activation of neural networks and coincides with the periods of increased firing rate in these networks. While information is retained in working memory – when the stimulus is no longer present but information about it is required for a further decision – an increased intensity of gamma oscillations is observed. This stands in contrast to the background state when there is no need to retain information in the working memory.
There are many computational models of working memory today, most of which are based on neural networks with multiple steady states. In the simplest case, the system has two steady states: the background state with a low firing rate (which corresponds to the absence of information in the working memory), and the active state with a high firing rate (which corresponds to the retention of information in the working memory). The transition from the background to the active state is triggered by an external impulse representing a to-be-retained stimulus. Recent research has demonstrated that the active state is stable only during short periods of time (this phenomenon is called ‘metastability’). In addition, it is known that the processes related to working memory retention are not localized in one area but distributed across the cortex.
In their paper, the authors considered a working memory model that contains a set of neuron populations linked by excitatory connections. The researchers assumed that the information about the presented stimulus is delivered to many populations of the prefrontal cortex, but only some of them participate in the stimulus retention assembling into a single functional network. Each population receives an input from the whole network: that is why various parts of the network receive similar input signals. This was modeled as a common noise input into some of the modeled populations (the remaining populations receive independent noise inputs).
Each of the populations had a stable background state and a metastable active state. The stability of working memory retention was evaluated as the average time in which a population returned to the background state after the presentation of a stimulus. The authors demonstrated that the retention is more robust in the group of populations that receive a common noise input as compared to the group of populations that receive independent noise inputs. The authors also discovered that gamma-band input caused working memory stabilization and amplified the difference between the two groups of populations, thus increasing the ‘fidelity’ of the retained information. The effect was most evident when the connections between the populations were fast and the gamma input was delivered to each population in the same phase.
Currently, the theoretical understanding of the role of brain oscillations in working memory is falling behind the accumulated experimental material. The paper by HSE researchers contributes further to the development of the theory describing oscillatory control of working memory processes.
Nikita Novikov, Junior Research Fellow at the Centre for Cognition & Decision Making
Our paper continues the series of theoretical studies that explore the link between neural oscillations and working memory, expanding the current understanding of this link. For example, classical papers in this field indicated the destabilizing role of oscillations and the importance of slow neural interaction for working memory retention. In our paper, we demonstrated the opposite effect — the stabilizing role of the oscillations and the importance of fast interaction. The results we obtained have the potential to become a part of the theoretical basis for experiments on improving working memory functions with non-invasive brain stimulation.
Nikita Novikov
Junior Research Fellow, Centre for Cognition & Decision Making
See also:
Smoking Habit Affects Response to False Feedback
A team of scientists at HSE University, in collaboration with the Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, studied how people respond to deception when under stress and cognitive load. The study revealed that smoking habits interfere with performance on cognitive tasks involving memory and attention and impairs a person’s ability to detect deception. The study findings have been published in Frontiers in Neuroscience.
'Neurotechnologies Are Already Helping Individuals with Language Disorders'
On November 4-6, as part of Inventing the Future International Symposium hosted by the National Centre RUSSIA, the HSE Centre for Language and Brain facilitated a discussion titled 'Evolution of the Brain: How Does the World Change Us?' Researchers from the country's leading universities, along with health professionals and neuroscience popularisers, discussed specific aspects of human brain function.
‘Scientists Work to Make This World a Better Place’
Federico Gallo is a Research Fellow at the Centre for Cognition and Decision Making of the HSE Institute for Cognitive Research. In 2023, he won the Award for Special Achievements in Career and Public Life Among Foreign Alumni of HSE University. In this interview, Federico discusses how he entered science and why he chose to stay, and shares a secret to effective protection against cognitive decline in old age.
'Science Is Akin to Creativity, as It Requires Constantly Generating Ideas'
Olga Buivolova investigates post-stroke language impairments and aims to ensure that scientific breakthroughs reach those who need them. In this interview with the HSE Young Scientists project, she spoke about the unique Russian Aphasia Test and helping people with aphasia, and about her place of power in Skhodnensky district.
Neuroscientists from HSE University Learn to Predict Human Behaviour by Their Facial Expressions
Researchers at the Institute for Cognitive Neuroscience at HSE University are using automatic emotion recognition technologies to study charitable behaviour. In an experiment, scientists presented 45 participants with photographs of dogs in need and invited them to make donations to support these animals. Emotional reactions to the images were determined through facial activity using the FaceReader program. It turned out that the stronger the participants felt sadness and anger, the more money they were willing to donate to charity funds, regardless of their personal financial well-being. The study was published in the journal Heliyon.
Spelling Sensitivity in Russian Speakers Develops by Early Adolescence
Scientists at the RAS Institute of Higher Nervous Activity and Neurophysiology and HSE University have uncovered how the foundations of literacy develop in the brain. To achieve this, they compared error recognition processes across three age groups: children aged 8 to 10, early adolescents aged 11 to 14, and adults. The experiment revealed that a child's sensitivity to spelling errors first emerges in primary school and continues to develop well into the teenage years, at least until age 14. Before that age, children are less adept at recognising misspelled words compared to older teenagers and adults. The study findings have beenpublished in Scientific Reports .
Meditation Can Cause Increased Tension in the Body
Researchers at the HSE Centre for Bioelectric Interfaces have studied how physiological parameters change in individuals who start practicing meditation. It turns out that when novices learn meditation, they do not experience relaxation but tend towards increased physical tension instead. This may be the reason why many beginners give up on practicing meditation. The study findings have been published in Scientific Reports.
Processing Temporal Information Requires Brain Activation
HSE scientists used magnetoencephalography and magnetic resonance imaging to study how people store and process temporal and spatial information in their working memory. The experiment has demonstrated that dealing with temporal information is more challenging for the brain than handling spatial information. The brain expends more resources when processing temporal data and needs to employ additional coding using 'spatial' cues. The paper has been published in the Journal of Cognitive Neuroscience.
Neuroscientists Inflict 'Damage' on Computational Model of Human Brain
An international team of researchers, including neuroscientists at HSE University, has developed a computational model for simulating semantic dementia, a severe neurodegenerative condition that progressively deprives patients of their ability to comprehend the meaning of words. The neural network model represents processes occurring in the brain regions critical for language function. The results indicate that initially, the patient's brain forgets the meanings of object-related words, followed by action-related words. Additionally, the degradation of white matter tends to produce more severe language impairments than the decay of grey matter. The study findings have been published in Scientific Reports.
New Method Enables Dyslexia Detection within Minutes
HSE scientists have developed a novel method for detecting dyslexia in primary school students. It relies on a combination of machine learning algorithms, technology for recording eye movements during reading, and demographic data. The new method enables more accurate and faster detection of reading disorders, even at early stages, compared to traditional diagnostic assessments. The results have been published in PLOS ONE.