Research suggests that neurons protect and preserve certain information through a dedicated area of ​​stable synapses

One of the brain’s most recognized qualities is its ability to adapt. Changes in neural circuits, whose connections are constantly adjusting as we experience and interact with the world, are essential to how we learn. But to keep our knowledge and memories intact, some parts of the circuits must resist this constant change.

“Brains have figured out how to navigate this landscape of balancing stability and flexibility, so that you can have new learning and lifelong memory,” says neuroscientist Mark Harnett, a researcher at MIT’s McGovern Institute for Brain Research.

In research published in Cell ReportsHarnett and his team show how individual neurons can contribute to both sides of this vital duality. By studying the synapses through which pyramidal neurons in the brain’s sensory cortex communicate, they have discovered how cells preserve their understanding of some of the most fundamental features of the world, while retaining the flexibility they need to adapt to a changing world.

Visual Connections

Pyramidal neurons receive information from other neurons through thousands of connection points. Early in life, these synapses are extremely malleable; their strength can change as a young animal absorbs visual information and learns to interpret it. Most remain adaptable into adulthood, but Harnett’s team found that some of the cells’ synapses lose their flexibility when the animals are less than a month old. Having synapses that are both stable and flexible means that these neurons can combine information from different sources to use visual information in flexible ways.

Postdoctoral fellow Courtney Yaeger has been closely studying these unusually stable synapses, which cluster along a narrow region of the elaborately branched pyramidal cells. She was interested in the connections through which the cells receive primary visual information, and so she traced their connections to neurons in a vision-processing center in the brain’s thalamus called the dorsal lateral geniculate nucleus (dLGN).

The long extensions through which a neuron receives signals from other cells are called dendrites. They branch off from the main body of the cell to form a tree-like structure. Spiny protrusions along the dendrites form the synapses that connect pyramidal neurons to other cells. Yaeger’s experiments showed that the connections in the dLGN all led to a defined region of the pyramidal cells, a narrow strip within what she describes as the trunk of the dendritic tree.

Yaeger discovered several ways in which synapses in this region, previously known as the apical oblique dendrite domain, differ from other synapses in the same cells. “They’re not that far apart, but they have completely different properties,” she explains.

Stable synapses

In a series of experiments, Yaeger activated synapses in pyramidal neurons and measured the effect on the cells’ electrical potential. Changes in a neuron’s electrical potential generate the impulses that cells use to communicate with each other. It is common for the electrical effects of one synapse to be amplified when neighboring synapses are also activated. But when signals were delivered to the apical oblique dendrite domain, each had the same effect, regardless of how many synapses were stimulated.

The synapses don’t interact with each other at all, Harnett explains. “They just do what they do. No matter what their neighbors are doing, they’re all doing pretty much the same thing.”

The team was also able to visualize the molecular content of individual synapses. This revealed a surprising absence of a certain type of neurotransmitter receptor, called an NMDA receptor, in the apical oblique dendrites. This is notable because of the role of NMDA receptors in mediating changes in the brain.

“Generally speaking, when you think about any type of learning, memory, and plasticity, it’s the NMDA receptors that are responsible for everything,” Harnett says. “It’s by far the most common substrate of learning and memory in all brains.”

When Yaeger stimulated the apical oblique synapses with electricity, generating patterns of activity that would strengthen most synapses, the team discovered a consequence of the limited presence of NMDA receptors. The strength of the synapses did not change. “From what we tested, there is no activity-dependent plasticity at this level,” Yaeger says.

This makes sense, the researchers explain, because connections in the thalamus cells transmit the primary visual information detected by the eyes. It is through these connections that the brain learns to recognize basic visual features such as shapes and lines.

“These synapses are actually a robust, very faithful readout of that visual information,” Harnett says. “That’s what they’re transmitting, and it’s not context-dependent. So no matter how many other synapses are active, they’re doing exactly what they’re going to do, and you can’t change them up or down based on activity. So they’re very, very stable.”

“Actually, these lines don’t have to be plastic,” Yaeger adds. “Can you imagine falling asleep and then forgetting what a vertical line looks like? That would be disastrous.”

By conducting the same experiments on mice of different ages, the researchers determined that the synapses that connect pyramidal neurons to the thalamus become stable a few weeks after the young mice first open their eyes. At that point, Harnett explains, they have learned everything they need to learn. In contrast, if the mice spend the first few weeks of their lives in the dark, the synapses never stabilize, further evidence that the transition depends on visual experience.

The team’s findings not only help explain how the brain balances flexibility and stability; they could also help researchers teach artificial intelligence how to do the same thing. Harnett explains that artificial neural networks are notoriously bad at this: When an artificial neural network that does something well is trained to do something new, it almost always experiences “catastrophic forgetting” and can no longer perform its original task. Harnett’s team is investigating how it can use what it has learned about real brains to overcome this problem in artificial networks.

This article is republished with kind permission from MIT News (web.mit.edu/newsoffice/), a popular site covering the latest research, innovation, and teaching at MIT.