Virtual reality goggles for mice

by Paul Curzon, Queen Mary University of London

Mouse wearing VR goggles adapted from an Image by Clker-Free-Vector-Images from Pixabay
Adapted from an Image by Clker-Free-Vector-Images from Pixabay

Conjure up a stereotypical image of a scientist and they likely will have a white coat. If not brandishing test tubes, you might imagine them working with mice scurrying around a maze. In future the scientists may well be doing a lot of programming, and the mice for their part will be scurrying around in their own virtual world wearing Virtual Reality goggles.

Scientists have long used mazes as away to test the intelligence of mice, to the point it has entered popular culture as a stereotypical thing that scientists in white lab coats do. Mazes do give ways to test intelligence of animals, including exploring their memory and decision making ability in controlled experiments. That can ultimately help us better understand how our brains work too, and give us a better understanding of intelligence. The more we understand animal cognition as well as human cognition, the more computer scientists can use that improved understanding to create more intelligent machines. It can also help neurobiologists find ways to improve our intelligence too.

Flowers for Algernon is a brilliant short story and later novel based on the idea, there using experiments on mice and humans to test surgery intended to improve intelligence. In a slightly different take on mice-maze experiments, Douglas Adams, in ‘The Hitchhikers Guide to the Galaxy’, famously claimed that the mice were actually pan-dimensional beings and these experiments were really incredibly subtle experiments the mice were performing on humans. Whatever the truth of who is experimenting on who, the experiments just took a great leap forward because scientists at Northwestern University have created Virtual Reality goggles for their mice.

For a long time researchers at Northwestern have used a virtual reality version of maze experiments, with mice running on treadmills with screens around them projecting what the researchers want them to see, whether mazes, predators or prey. This has the advantage of being much easier to control than using physical mazes, and as the mice are actually stationary the whole time , just running on a treadmill, brain-scanning technology can be used to see what is actually happening in their brains while facing these virtual trials. The problem though is that the mice, with their 180 degree vision, can still see beyond the edges of the screens. The screens also give no sense of 3 dimensions, when like us the mice naturally see in 3D. As the screens are not fully immersive, they are not fully natural and that could affect the behaviour of the mice and so invalidate the experimental results.

That is why the Northwestern researchers invented the mousey VR googles, the idea being that they would give a way to totally immerse the mice in their online world, and so improve the reliability of the experiments. In the current version the goggles are not actually worn by the mice, as they are still too heavy. Instead, the mouse’s head is held in place really close to them, but with the same effect of total immersion. Future versions may be small enough for the mice to wear them though.

The scientists have already found that the mice react more quickly to events, like the sight of a predator, than in the old set-up, suggesting that being able to see they were in a lab was affecting their behaviour. Better still, there are new kinds of experiment that can be done with this set up. In particular, the researchers have run experiments where an aerial predator like an owl appears from above the mice in a natural way. Mounting screens above them previously wasn’t possible as it got in the way of the brain scanning equipment. What does happen when a virtual owl appears? The mice either run faster or freeze, just as in the wild. This means that by scanning their brains while this is happening, how their perception of the threat works can be investigated, as well as how decision-making is taking place at the level of their brain activity. The scientists also intend to run similar experiments where the mouse is the predator, for example chasing a virtual fly too. Again this would not have been possible previously.

That in any case is what we think the purpose of these new experiments is. What new and infinitely subtle experiments it is allowing the pan-dimensional mice to perform on us remains to be seen.

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EPSRC supports this blog through research grant EP/W033615/1. 

Exploring mazes, inventing algorithms (part I) 

by Paul Curzon, Queen Mary University of London

A maze with mouse searching for cheese.
Image by CS4FN

Computer science research in part involves inventing new algorithms or improving new ones. But what does that mean. Let’s explore some mazes to explore algorithms.

What does computer science research involve? It is very varied: from interviewing people to find out what the real problems that need solving in their lives or jobs are; to running experiments to find out what works and what doesn’t; to writing programs to solve problems.

Improving algorithms

A core part of much research is coming up with new and better algorithms that solve particular problems. The kind of algorithm could be anything from a new more secure cryptographic protocol, or a better way to rank the results of a search engine, to a new more effective machine learning algorithm that is less likely to make things up, or perhaps can better explain how it came to its conclusions.

What does it mean to come up with a better algorithm though? Once a problem is solved, isn’t it solved? Let’s explore a simple problem to see. Let’s explore mazes. Solve the simple maze puzzle above before you go on. Find a route that gets the mouse to the cheese.

Wandering around mazes, finding algorithms

If you’ve ever been in a hedge maze in the garden of some stately home, or a corn field maze, the chances are you just dived in and wandered rather aimlessly. Perhaps you tried to remember which way you went at each junction, to avoid going down the same dead-ends more than once. How about solving the paper version of a maze puzzle like the one above? Now perhaps you looked ahead to spot dead-ends to avoid tracing wrong paths with your pencil.

Probably what you are doing is at least a little random. You could, in theory at least, end up going back over the same paths, never taking the right one and and never getting to the middle. Could we come up with an algorithm that guarantees to solve mazes? To be an algorithm it would need to guarantee you ended up finding a path to the centre of the maze if you followed the steps of the algorithm precisely. It should also work for any maze, or at least all mazes of a particular kind. Ideally, the algorithm gives you a path that can then be followed by anyone without them having to run the algorithm themselves. They can just follow the path generated by the algorithm for that maze.

Wall-following

In fact lots of maze algorithms have been invented. Perhaps the one most people have heard of, if they know of any maze algorithm, is called Wall-following. It is very simple to do, You just pick a wall at the entrance either to the left or right and then follow it, If in a garden maze, keep your hand on the hedge as you walk round. If doing a paper puzzle, draw the path sticking to the chosen wall. Try it on the following simple maze.

A simple maze with mouse and cheese.
Image by CS4FN

Simply connected

The wall-following algorithm will guarantee to get you to the centre of the maze, and back out again too, but only as long as the maze is what is called simply connected. That just means the maze is constructed from a single hedge (or one unbroken drawn line) not a series of unconnected hedges. If you look at both examples above you will see I created them by just drawing a single wiggly line.

If a maze is simply connected then it cannot have looping paths, so no going round in circles for ever. It will also only have one entrance/exit. That shows the first aspect of inventing algorithms that is important. They often only work for some situations, not all. You must be sure you know what situations they do and don’t work.

Often the earliest algorithms invented to solve a problem are like wall-following: they only work for simple situations. Other people then come along and find new algorithms that can cover more problems (here more mazes). Can you tweak the wall-following maze algorithm to work even if there are multiple exits from the maze, for example? As it stands our algorithm could just take you from the entrance straight out of another exit without exploring much of the maze at all! See the end for one simple way to tweak the algorithm. What if there are paths that take you round in circles? Can you come up with an algorithm to deal with that?

Some times the improvements invented just involve tweaking an existing algorithm as with dealing with multiple exits in a maze. Some times a whole new algorithm is needed.

Faster, higher, stronger?

Even for a simple constrained version of the problem, like simply connected mazes, people can invent better algorithms. What does better mean for a maze? Well one way you might have a better algorithm is if it is faster in coming up with a solution. Another is that the solution it comes up with is faster. For a maze that means a shorter (ideally the shortest) path to the centre. Wall following may get you in to the centre (and out again) but you probably will have discovered a very long path that takes you in and out of lots of dead-ends needlessly. You do find a path to the centre, but it may be a very long path. Can you come up with an algorithm that finds shorter paths?

We will explore an algorithm that does next.

More to come…

Some solutions

The result of wall following on our simple maze

A route for the mouse to follow that takes it to the cheese.
Image by CS4FN

One way to deal with multiple exits

To deal with a maze that has multiple exits, so multiple breaks in the outer wall, tweak the wall-following algorithm as follows. First mark the exit you use to enter the maze, so you know when you return to it. If you come to any other exit then pretend there is a gate there and keep following the wall as though it were unbroken and there were no exit.

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EPSRC supports this blog through research grant EP/W033615/1.