Ricardo Opined

[Garland Flugum]

Inthis "Artificial Intelligence & Equality" podcast, Senior Fellow Anja Kaspersen talks with Dr. Ricardo Chavarriaga about the promise and peril of brain-machine interfaces and cognitive neural prosthetics. What are the ethical considerations and governance challenges in using computational tools to create models or enhance our brains?

My guest today is Ricardo Chavarriaga. Ricardo is an electrical engineer and a doctor of computational neuroscience. He is currently the head of the Swiss office of the Confederation of Laboratories for AI Research in Europe (CLAIRE) and a senior researcher at Zurich University of Applied Sciences.

RICARDO CHAVARRIAGA: Certainly. When we think about the brain, this is something that has fascinated humanity for a long time. The question of how this organ that we have inside our heads can rule our behavior and can store and develop knowledge has been indeed one of the questions for science for many, many years. Neurotechnologies, computational neuroscience, and brain-machine interfaces are tools that we have developed to approach the understanding of this fabulous organ.

When we talk about computational neuroscience it is the use of computational tools to create models of the brain. It can be mathematical models, it can be algorithms that try to reproduce our observations about the brain. It can be experiments on humans and on animals: these experiments can be behavioral, they can involve measurements of brain activity, and by looking at how the brains of organisms react and how the activity changes we will then try to apply our knowledge to create models for that.

These models can have different flavors. We can for instance have very detailed models of electrochemical processes inside a neuron, and then we are looking at just a small part of the brain. We can have large-scale models with fewer details of how different brain structures interact among themselves, or even less-detailed models that try to reproduce behavior that we observe in animals and in humans as a result of certain mental disorders. We can even test these models using probes to tap into how can our brain construct representations of the world based on images, based on tactile, and based on auditory information. So in computational neuroscience we are combining the knowledge that we get from cognitive and clinical neuroscience with mathematical tools with knowledge about statistics and computer science as a way to better understand the brain.

As I said, some of these studies are based on measuring the brain or stimulating the brain or somehow manipulating the brain. It can be in vitro or it can be in vivo. For that we use technology, and this is what we broadly call neurotechnologies, technologies that can interact with the brain, sensing, recording, and stimulating.

Among these neurotechnologies we have brain-machine interfaces. They will have a set of sensors. They can be implanted within the brain. They can be placed on your scalp and will provide us information about brain activity.

This activity is then processed using artificial intelligence methods and machine learning methods to try to extract information from that activity. This information can be, for instance, a particular state of the person: is he sleepy, is he less attentive; or intentions: does the person want to move a hand or a foot? When we have extracted this information it can be used as a command to control a device.

Let's imagine the case of a person who is heavily paralyzed. This is a particular condition called locked-in syndrome, where people lose control of their muscles but retain their cognitive capabilities. These people can modulate their brain activity, they can hear, they can produce speech, but they cannot speak because they cannot move their muscles.

If we can take information about these intentions using these neurotechnologies, we can then connect it to a communication device and provide them the capacity to communicate with others. There have been several studies along these lines. This is one example in the realm of assistive devices. We can have brain-computer interfaces used as prosthetic systems, a robotic arm, a robotic wheelchair.

Last but not least, there is also interest in military applications. Some of these include trying to identify people who can be more likely to develop post-traumatic stress disorder, having technologies to diagnose brain injury on the battlefield, or also as cognitive enhancement to increase or maintain higher levels of attention for soldiers.

I have to say that most of these systems are currently at the research stage, so most of them are still being developed and tested in research laboratories. There are only a handful of them that are now in the commercialization stage or close to commercialization, but in the general sense there are still many questions and many needs to properly assess their safety, their efficacy, and their performance in real-life applications.

We are at the moment where the field is moving from this research stage, this early age of neurotechnologies, towards the technology translation phase and trying to realize the potential of these technologies and bring them to the users. That makes it quite exciting to work in this domain today.

ANJA KASPERSEN: I would like to follow up on the distinction you just made between in vitro and in vivo experiments. Can you elaborate a bit more on what the difference is and why it is important to understand what the difference is also from an ethical point of view?

RICARDO CHAVARRIAGA: When we talk about in vitro experiments we are basically referring to experiments in cultures of cells. These are neural cells, neurons, that are extracted from a brain and are kept in a solution so that they remain alive for some time, but they are detached from the body and from other brain structures. This allows us to characterize the properties of a neuron or how a small population of neurons can interconnect among themselves and how they react to certain interventions, but they don't provide us information about how this activity is linked to behavior or to other characteristics of an entire entity.

When we talk about in vivo we are talking about settings where we can measure the activity of the brain in a living entity. It can be an animal model or it can be a human. These measurements can be done with implanted electrodes. For instance, people who go through brain surgery or epilepsy sometimes remain in the hospital with electrodes implanted to measure the activity of the brain and to identify areas of the brain that are linked or are the source of the epileptic seizures. This information is used to better understand the processes that are going on.

This can also be measured with sensors that are placed on the scalp. One of the most common technologies is electroencephalography (EEG), which has been around for more than 100 years and is commonly used in clinical sittings. It allows us to measure certain activity of the brain as well. These will be the in vivo experiments.

When we talk about brain-machine interaction and the possibility of interacting with prosthetic devices or communication devices, we are talking about the second type of settings, where in this case the entire person, an entity, has sensors.

ANJA KASPERSEN: Thank you, Ricardo. Listening to you talk and share your passion for this field brings me to my next question for you, and also to allow our listeners to get to know you a bit better: What, and if relevant who, sparked your interest in the brain and the vast field of computational neuroscience?

I have been strongly inspired by my parents. Despite not being a wealthy family, they really valued education and learning throughout my entire life. My father and mother were both the first generation in their families to go to university, and at the time among the people I knew we were just one of a handful of households where both parents worked. That somehow embedded in me this love for improving and looking for new opportunities to learn about the environment and to apply it.

At the same time, I grew up in Colombia in the 1990s. Colombia is a country that has a long history marked by violence. In the 1990s it was an environment where the civil society was basically trapped between fights among the drug cartels, the right-wing paramilitary, and the left-wing guerillas. This made me conscious of the impact of the power imbalance among these different entities, the inequality, and at the same time how privileged I was of having access to education, to have been somehow spared from most of this violence, even though no one in this environment can say he was completely spared. At the same time that made me think about the responsibility that we have to contribute our knowledge to make our society a little bit better.

At the same time it was a booming era of personal computers coming in, robotics being developed, and more exchange of knowledge and communication between people thanks to a recent innovation at the time called the Internet. It was just amazing how we could use these technologies to bring people together to create machines that could support humans in their goals and dreams, and that led me to study engineering. I got hooked on the idea of putting biology and machines together and create bio-inspired machines. That was my first stage. I was really interested in doing research.

At some point I wanted to look for opportunities to expand my knowledge about biology, about science, and about the world, so I looked for opportunities and got the chance to come to Switzerland with a scholarship. I came thinking about artificial neural networks. It was right before the "AI winter" started. This seemed like an extremely interesting idea for making machines that could adapt and could learn.

When I came here and started to look at these artificial neural networks and saw how I could also study biological neural networks. I thought that sounded even better, this possibility of taking all the ideas about machines and then seeing to what extent they could apply for modeling the brain.

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