They no longer look for patterns in how neurons connect but instead study the brain as a collection of transistors—biochemical machines that are turned on or off by neurotransmitters.
The same molecules used to transmit signals across neural circuits also provide machinery for the cells to communicate with one another.
At MIT’s Picower Institute for Learning and Memory, neuroscientists are exploring how neurons encode information.
It’s a question that connects neuroscience to computer science, which is currently designing chips that mimic the brain’s neural software.
The idea is to reverse-engineer the brain by extrapolating from what we know about its components and functions.
But one of the biggest challenges in this kind of research is that the brain is so complex.
“It’s not like we can go in and remove a chunk of tissue and study it in a dish,” said Susumu Tonegawa, the Picower Professor of Biology and Neuroscience and director of the RIKEN-MIT Center for Neural Circuit Genetics at MIT. “We have to rely on the animal model. And how relevant is what we learn from an animal model to human behavior?”
In a new study, Tonegawa and colleagues at MIT’s McGovern Institute for Brain Research have taken a step toward answering this question by comparing mice with a form of autism to those without the disorder.
The researchers looked at mice that were genetically engineered to lack a gene called Shank3, which is known to be involved in some cases of autism.
When did we first start accomplishing this?
Scientists have been struggling to understand the workings of the brain for centuries.
The first attempts were mainly done through dissection and observation of brain pathologies. In the early 1800s, Franz Gall began to develop phrenology, which was the belief that different areas of the brain controlled different functions or personality traits.
This theory was debunked in the 1830s by French anatomist Pierre- Paul Broca, who showed that isolated injuries to specific regions of the brain led to deficits in language.
Even though there is still much to learn about how the brain works, science has come a long way since then.
For one thing, it has found ways to peer inside living brains with imaging techniques like fMRI (functional magnetic resonance imaging).
These techniques can track changes in brain activity by measuring changes in blood flow.
Scientists have also developed methods to study the genes that are active in specific cells or groups of cells, as well as manipulating individual neurons with light.
How does MRI work?
One advantage of studying the brain through models is that we can study the brains of lab mice. We can do this because we know that, like humans, their brains work in a similar way and function on the same basic principles.
However, most neuroscientists agree that we still have a long way to go before we fully understand how the brain encodes information.
The complexity of the processes involved is daunting, to say the least.
This is where computer science comes in. Computer scientists are experts at breaking down complex processes into their individual parts and designing systems that can carry out those processes.
This is what they are doing with the brain.
They are taking the information we know about how the brain works and designing chips that can mimic that function in an analogous way.
What are the advantages of using mice?
For one thing, it is possible to study in detail how brain activity changes during specific tasks.
Researchers can also easily control the genetics of mice by knocking out or adding genes that may play a role in various behaviors.
And they can do these manipulations throughout the mouse’s life, which is within the reach of many lab instruments.
All of this means that scientists can study mice over time and determine how mutations may affect their behavior as they age.
Tonegawa’s work relies on a technique called optogenetics, which allows researchers to control neuronal activity with light by engineering neurons to express light-sensitive proteins.
In this case, the researchers engineered mice to express these light-sensitive proteins only in the neurons that release the neurotransmitter GABA.
When did optogenetics become available?
This is an important point because brain activity is controlled by populations of cells, not just individual cells.
To understand how these population activities work, scientists need to be able to target specific populations of cells.
This is where optogenetics comes in. Optogenetics allows scientists to target specific cells and control their activity with light. This is a powerful tool because it provides a way to selectively activate or inhibit different cell populations.
How does optogenetics work?
The first step is to engineer neurons to express light-sensitive proteins.
Then, using a light source that is implanted or attached to the skull of the animal, researchers can control neuronal activity by switching on or off this light.
This technique was first developed in the 1970s, but it wasn’t until 2005 that Karl Deisseroth and colleagues published a paper describing how to use light-sensitive molecules like channelrhodopsin protein (ChR) as tools for manipulating neuronal activity.
They showed how they could selectively activate neurons using blue light.
Optogenetics involves engineering neurons in living mice to express light-sensitive proteins.
By implanting a light source in the animal’s skull, researchers can switch on or off the neuronal activity by turning the light on or off.
The brain is the central organ in the nervous system. It composes of many parts and components, all working together to process and transmit information.
To understand how this works, we need to study individual parts or groups that work together.
This can be done through simplified models such as computer chips that mimic neurons and neuronal circuits.
We use mice for this research because they offer certain advantages, such as the ability to study behavior over time and control genetics.
The use of optogenetics has revolutionized neuroscience by giving scientists a way to selectively activate or inhibit different cell populations.
This is important for understanding how population activities work.
We have come a long way in understanding the brain, but there is still much to learn.
The study of the brain is a complex and difficult task, but with the help of tools like computer chips and optogenetics, we are making progress.
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