The Honey Bee Brain | Dr. Andrew Barron | Stories of Impact

Many things are impressive about the honeybee. When you work this closely, you see their intelligence, you see their individuality, you see their collective behavior, you see the structures they've built, you see the organization of that society. You can't do anything but admire it. They are the most beautiful, phenomenal creatures, they really are.

So this bee has learned that if it moves that yellow ball into the yellow circle, the well beneath the ball fills up with nectar and it gets a drink. All of that intelligence, all of that smarts, comes somehow from the bee brain, and I want to understand this bee level of intelligence. So we have a jumbo jet, we have a bumblebee, we have an osprey. I could not say which one is a better flier than the other because they're different.

Putting in the envelope about what intelligence is is extremely difficult. I think what will help us frame that envelope is if we can study the diversity. So if we study intelligence not just in humans but in other living things, potentially even other machines, we can tidy up that definition of what intelligence is and where we draw the boundary on what's intelligent and what's not. My project is particularly focusing on honeybee intelligence because it gives us such an informative comparative lens on the intelligence of other animals, including humans. Things like complex learning, complex memory, complex navigation, complex assessment — we'll learn some evolved solutions for that, and we can then ask: is the human brain doing this in a similar way?

We have these tiny little animals with really minute brains. They have a million neurons. It's minuscule compared to a human brain.

The bumblebee brain is very small, but it would be wrong to characterize it as a simple system. We do still have one million neurons in a bee brain, and they are organized in quite beautiful, different structural regions that interact and intersect in very complex ways. People normally think they're very clever as groups but simply rather stupid individually, and nothing could be further from the truth.

Honeybees have been documented to find their way home from twelve kilometers away. In a routine foraging flight, bees will fly five or six kilometers, which doesn't sound much, but when you scale that by the size of an individual bee, that's a really huge distance. Our own machine learning and their algorithms for navigation just aren't as sophisticated or reliable. But what stands out as a unique feature of the honeybee would have to be its symbolic dance language. When they dance, the vigor with which they shake their butt and how many times they dance is the quality of the sugar reward. We found they are transforming information about distance and direction to things in the real world, to these remote food sources, into a single vector that they cleanse signal through a dance. So the dance is a readout of this subjective evaluation of how good that reward was for the bee. It's the tail waggle for a bee.

So for me, the bee was in this unique position where its behavior was complex enough to be interesting, but its neurobiology and its brain was simple enough that we could study it.

The honeybee's really a spectacular learner. They learn very fast and very robustly. As an example, if we give a honeybee something simple to learn, like this odor is associated with nectar, this odor is where you find nectar, it will learn that on one trial. If you give it three trials, it will learn that for the rest of its lifetime. So that's very fast acquisition of relationships between information. They can even learn things that we would consider to be abstract concepts, things that we would call learning of sameness, learning of difference. Honeybees are able to do that. That hasn't been shown in any other invertebrate that I know of.

A statement 'I don't know' is an example of metacognition — you're assessing a circumstance and you're coming to the conclusion that you don't have enough information to address that or to answer that.

If we look comparatively across the literature in many tests, even these tests of very simple learning or even tests of very complex learning, we see that bees learn faster than rats. I don't have an answer for you as to why that is yet. It fascinates me. We have an organism where our assumption is this is smarter, and yet in a whole battery of learning tests, memory tests, of spatial cognition, the bees are outperforming. If the bee is solving a test that we think demonstrates metacognition, how can an animal with just one million neurons do that? It forces us to rethink our assumptions. What is the minimal computational architecture that could do this?

A computational model is building a circuit diagram of the brain in a virtual world, and we can then make it a dynamic system that we can feed input to. It will process the input in the way that we think the honeybee brain is processing it, and it will give us an output. We can analyze that output in terms of: is this system doing what the bee's doing? If it is, maybe our model is close to reality. We can do exactly the same with bits of the mammalian brain, and that means that we can actually compare what are superficially very, very different-looking systems. We've done something that no one else has done in that we've taken an abstract concept and we have given you a neuron-by-neuron connected circuit.

So if we can model the bee brain, we can take insights from those models and translate them directly into technological applications.

So we're building drones that can fly in a comparable way to a bee, but not exactly the same as a bee. You know, with only a million neurons in the bee brain, they were already well in advance of our own abilities in artificial intelligence and robotics. So really what we'd like to do is try and make kind of silicon versions of bee brains, or at least of the aspects of the bee brains that generate behavior we find useful for our own robots. So especially around navigation. I thought if we could just reverse engineer the bee brain, that could actually try and really advance the state of the art. Bees have evolved for millions of years to be fantastic autonomous behavioral control systems. They're really robust, they're really reliable, they're amazing navigators across very large distances. All of these are current challenges in autonomous robotics, and yet the bee's doing it with incredible computational efficiency.

In particular, we want to be able to reproduce, for example, the collision avoidance or navigation abilities of bees in robot form. Let's imagine autonomous drones that we could use in exploration, or agriculture, or in mining. At least eight people have been killed after a magnitude 6.1 earthquake struck the Philippines, for example, trying to deploy drones to search for survivors. But in an earthquake or something like that, time is going to be of the essence. You want to automate as much of the process as possible, have fully autonomous flight and navigation for the robots. Then we could have some real benefits. And that kind of technology would be the holy grail for so much robotics.

The bee's solved that with this minute brain. We're finding that bee navigation may actually be a lot more map-like than people have previously assumed. I mean, the idea of a mental map is that you have kind of representation of the relationship between points in space. That seems like a much higher-level kind of cognitive ability people have typically assumed bees and other insects are able to employ. We've been looking at now, Griffin, inspired by how the honeybee brain works, what's called optic flow estimation, which just basically tells you how fast things are moving across the visual field. And you can use that — you know, as you will have seen from looking out the window on a train, for example — when things are close to you, they move much faster apparently across your visual field. You can use that as depth information, a depth cue, or information that you're about to crash into something. But you can also use it for a variety of other applications, like using it to estimate how far you've traveled or how fast you're traveling. And again, these are tremendously useful for navigation and for flight control.

Whether we like it or not, we're in this robotic revolution. It's happening. It will only accelerate even further. What interests me is the capacity for safe robotics. If we're going to have a system that is trustworthy, we need to understand how that system works very, very deeply. If we're starting our robotic systems on the basis of a deeply understood system like the bee brain, to me, we have a system that is more intrinsically understood, and I think, therefore, potentially safer and more trustworthy than some of the current approaches in robotics.

I suspect I'm not alone in saying this, but I think that in the arc of understanding of the bee brain, we're at the most exciting point. We're really getting to the point where we can put not just the bee brain but insect brains together as an information flow system. Just being able to translate what we've learned from the bee as a hypothesis to help us analyze a human brain and mammalian brains — that's the value of the work I'm doing with bees.

We all have an attachment to cats and dogs because they're so naturally empathic. When you just look at Abby's face, it gives nothing away. It gives you nothing. The face is a blank mask. I have as warm a relationship with bees because I developed so much respect for them. When I work with bees, usually I'm working with just one individual bee who I've paint-marked or number-marked so I know who she is. In the course of that day, you get this really privileged insight into the kind of intelligence that this animal has, and you realize just how astonishing it is and just what a cognitive and elegant and beautiful entity this animal is.