Dr. Terry Sejnowski

So there's an intermediate level between the implementation level at the bottom, how you implement some particular mechanism. And the actual behavior of the whole system is called the algorithmic system. level. It's in between. So algorithms are like recipes. They're like when you bake a cake, you have to have ingredients and you have to say the order in which they're put together and how long.

And if you get it wrong, it doesn't work. It's just a mess. Now, it turns out that we're discovering algorithms. We've made a lot of progress. with understanding the algorithms that are used in neural circuits. And this speaks to the computational level of how to understand the function of the neural circuit.

And if you get it wrong, it doesn't work. It's just a mess. Now, it turns out that we're discovering algorithms. We've made a lot of progress. with understanding the algorithms that are used in neural circuits. And this speaks to the computational level of how to understand the function of the neural circuit.

And if you get it wrong, it doesn't work. It's just a mess. Now, it turns out that we're discovering algorithms. We've made a lot of progress. with understanding the algorithms that are used in neural circuits. And this speaks to the computational level of how to understand the function of the neural circuit.

But I'm going to give you one example of an algorithm, which is one we worked on back in the 1990s when Peter Dayan and Reed Montague were postdocs in the lab. And it had to do with a part of the brain below the cortex called the basal ganglia, which is responsible for learning sequences of actions in order to achieve some goal.

But I'm going to give you one example of an algorithm, which is one we worked on back in the 1990s when Peter Dayan and Reed Montague were postdocs in the lab. And it had to do with a part of the brain below the cortex called the basal ganglia, which is responsible for learning sequences of actions in order to achieve some goal.

But I'm going to give you one example of an algorithm, which is one we worked on back in the 1990s when Peter Dayan and Reed Montague were postdocs in the lab. And it had to do with a part of the brain below the cortex called the basal ganglia, which is responsible for learning sequences of actions in order to achieve some goal.

For example, if you want to play tennis, you have to be able to coordinate many muscles and a whole sequence of actions has to be made if you want to be able to serve accurately. And you have to practice, practice, practice. Well, what's going on there is that the basal ganglia basically is taking over from the cortex and producing actions that get better and better and better and better.

For example, if you want to play tennis, you have to be able to coordinate many muscles and a whole sequence of actions has to be made if you want to be able to serve accurately. And you have to practice, practice, practice. Well, what's going on there is that the basal ganglia basically is taking over from the cortex and producing actions that get better and better and better and better.

For example, if you want to play tennis, you have to be able to coordinate many muscles and a whole sequence of actions has to be made if you want to be able to serve accurately. And you have to practice, practice, practice. Well, what's going on there is that the basal ganglia basically is taking over from the cortex and producing actions that get better and better and better and better.

And that's true not just of the muscles, but it's also true of thinking. If you want to become good in any area, if you want to become a good financier, if you want to become a good doctor, or neuroscientists, right? You have to be practicing, practicing, practicing in terms of understanding the details of the profession and what works, what doesn't work, and so forth.

And that's true not just of the muscles, but it's also true of thinking. If you want to become good in any area, if you want to become a good financier, if you want to become a good doctor, or neuroscientists, right? You have to be practicing, practicing, practicing in terms of understanding the details of the profession and what works, what doesn't work, and so forth.

And that's true not just of the muscles, but it's also true of thinking. If you want to become good in any area, if you want to become a good financier, if you want to become a good doctor, or neuroscientists, right? You have to be practicing, practicing, practicing in terms of understanding the details of the profession and what works, what doesn't work, and so forth.

And it turns out that this basal ganglia interacts with the cortex, not just in the back, which is the action part, but also with the prefrontal cortex, which is the thinking part.

And it turns out that this basal ganglia interacts with the cortex, not just in the back, which is the action part, but also with the prefrontal cortex, which is the thinking part.

And it turns out that this basal ganglia interacts with the cortex, not just in the back, which is the action part, but also with the prefrontal cortex, which is the thinking part.

Well, I mentioned the prefrontal cortex and that part, the loop with the basal ganglia, that is one of the last to mature in early adulthood. And the problem is that for adolescents, it's not the no-go part for planning and actions isn't quite there yet. And so often it doesn't kick in to prevent you from doing things that are not in your best interest. So yes, absolutely right.

Well, I mentioned the prefrontal cortex and that part, the loop with the basal ganglia, that is one of the last to mature in early adulthood. And the problem is that for adolescents, it's not the no-go part for planning and actions isn't quite there yet. And so often it doesn't kick in to prevent you from doing things that are not in your best interest. So yes, absolutely right.

Well, I mentioned the prefrontal cortex and that part, the loop with the basal ganglia, that is one of the last to mature in early adulthood. And the problem is that for adolescents, it's not the no-go part for planning and actions isn't quite there yet. And so often it doesn't kick in to prevent you from doing things that are not in your best interest. So yes, absolutely right.

But one of the things, though, is that learning is involved. And this is really a problem that we cracked. first theoretically in the 90s and then experimentally later by recording from neurons and also brain imaging in humans. So it turns out we know the algorithm that is used in the brain for how to learn sequences of actions to achieve a goal.