With each turn you take through a deck of vocabulary word flashcards, their definitions come quicker, easier. This process of learning and remembering new information strengthens important connections in the brain. Remembering those new words and definitions more easily with practice is evidence that those neural connections, called synapses, can become stronger or weaker over time, a feature known as synaptic plasticity.
Enhance brain memory
Quantifying the dynamics of individual synapses can be a challenge for neuroscientists, but recent computational innovations from the Salk Institute could change that and reveal new insights into the brain along the way.
To understand how the brain learns and retains information, scientists try to quantify how much stronger a synapse has become through learning and how much stronger it can become. Synaptic strength can be measured by looking at the physical characteristics of synapses, but it is much more difficult to measure the precision of plasticity (whether synapses consistently weaken or strengthen) and the amount of information a synapse can store.
Salk scientists have created a new method to explore synaptic strength, the precision of plasticity, and the amount of information stored. Quantifying these three synaptic characteristics can improve scientific understanding of how humans learn and remember, as well as how those processes evolve over time or deteriorate with age or disease. The results were published on Neural Computation.
“We are getting better at identifying exactly where and how individual neurons are connected to each other, but we still have a lot to learn about the dynamics of those connections,” says Professor Terrence Sejnowski, senior author of the study and owner of the project. Francis Crick Chair at Salk.
Salk’s team applied concepts from information theory to analyze pairs of synapses in a rat’s hippocampus – a part of the brain involved in learning and memory – for strength, plasticity and precision.
Information theory is a sophisticated mathematical way of understanding the processing of information as input that travels through a noisy channel and is reconstructed at the other end.
Crucially, unlike methods used in the past, information theory takes into account the noisiness of the brain’s many signals and cells, as well as offering a discrete unit of information – a bit – to measure the amount of information stored in a synapse .
“We divided synapses by strength, of which there were 24 possible categories, and then compared special pairs of synapses to determine how precisely the strength of each synapse is modulated,” says Mohammad Samavat, first author of the study and researcher postdoctoral fellow in Sejnowski’s lab.
“We were excited to find that the pairs had very similar dendritic spine sizes and synaptic strength, meaning that the brain is highly precise when it makes synapses weaker or stronger over time.”
In addition to noting similarities in synapse strength within these pairs, which translates to a high level of plasticity precision, the team also measured how much information was contained in each of the 24 strength categories. Despite differences in the size of each dendritic spine, each of the 24 categories of synaptic strength contained a similar amount of information (between 4.1 and 4.6 bits).
Compared to older techniques, this new approach using information theory is (1) more thorough, taking into account 10 times more information stored in the brain than previously assumed, and (2) scalable, the which means it can be applied to diverse and large datasets to gather information about other synapses.
“This technique will be of enormous help to neuroscientists,” says Kristen Harris, a professor at the University of Texas at Austin and an author of the study. “Having this detailed look at synaptic strength and plasticity could really boost learning and memory research, and we can use it to explore these processes in all different parts of the human brain, the animal brain, the young brain, and the brain. old.”
Sejnowski says future work from projects like the National Institutes of Health’s BRAIN Initiative, which created an atlas of human brain cells in October 2023, will benefit from this new tool.
In addition to scientists cataloging the types and behaviors of brain cells, the technique is of interest to those who study when information storage goes awry, as in Alzheimer’s disease.
In the years to come, researchers around the world could use this technique to make exciting discoveries about the human brain’s ability to learn new skills, remember everyday actions, and store short- and long-term information.
Nearby synapses shape learning and memory
A researcher from the University of Basel, in collaboration with an Austrian colleague, has developed a new model that provides a holistic view of how our brain manages to learn quickly and form stable, long-lasting memories.
Their study sheds light on the crucial role of interactions between nearby contact sites of nerve cells for brain plasticity, the brain’s ability to adapt to new experiences.
In 1949, Canadian psychologist Donald O. Hebb described that connections between neurons become stronger when neurons are active at the same time, and that strengthened connections facilitate signal transmission. Our brain’s ability to change connections between neurons is critical for learning and memory.
“It has long been assumed that these adaptations mostly occur one-to-one at specific synapses, the points of contact between two neurons,” explains Dr. Everton Agnes of the Biozentrum of the University of Basel. “Interestingly, synapses that undergo changes also affect multiple nearby synapses.”
Since these complex synaptic interactions are difficult to study experimentally, Agnes and her colleague Prof. Tim Vogels from the Institute of Science and Technology Austria have built a theoretical model to untangle this phenomenon, also known as co-dependency. Their work was published in Nature Neuroscience.
We’ve all known it since school: when you learn new words repeatedly, you can remember them better. This is because the neurons involved in processing this information form stronger connections with each other over time.
These changes in synaptic connections – both strengthening and weakening – are known as synaptic plasticity. In this way, the brain continually updates its neuronal network to store new information or remove irrelevant information, the basis for learning and memory.
Neurons are mostly connected by excitatory and inhibitory synapses. While excitatory synapses transmit a signal, inhibitory synapses reduce signal transmission.
“Different types of synapses not only function independently, but neighboring synapses influence each other, thus shaping the strength and stability of neuronal connections,” explains Agnes.
“With our model, we could reveal, for example, that interactions between nearby excitatory synapses determine the strength of connections, linked to how memories are encoded.”
Complementary and inhibitory synapses represent the long-lasting stability of excitatory synaptic changes, providing the mechanism necessary for one-shot learning, when memories are learned after a single exposure.
The precise interaction between neighboring synapses is critical for rapid learning and the formation of long-lasting memories.
“By integrating a large set of rules regarding synaptic co-dependency into our network model, we provide a more holistic view on the mechanisms underlying brain plasticity,” emphasizes Agnes. The study highlights the importance of neighborhood interactions and provides new insights into the dynamics and tuning of neural networks in the microscale brain.
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