̽��ֱ�� of Cambridge - movement

Algae use their ‘tails’ to gallop and trot like quadrupeds

sc604 — Tue, 03 May 2016 14:12:54 +0000

Long before there were fish swimming in the oceans, tiny microorganisms were using long slender appendages called cilia and flagella to navigate their watery habitats. Now, new research reveals that species of single-celled algae coordinate their flagella to achieve a remarkable diversity of swimming gaits.

When it comes to four-legged animals such as cats, horses and deer, or even humans, the concept of a gait is familiar, but what about unicellular green algae with multiple limb-like flagella? ̽��ֱ��latest discovery, published in the journal Proceedings of the National Academy of Sciences, shows that despite their simplicity, microalgae can coordinate their flagella into leaping, trotting or galloping gaits just as well.

Many gaits are periodic: whether it is the stylish walk of a cat, the graceful gallop of a horse, or the playful leap of a springbok, the key is the order or sequence in which these limbs are activated. When springboks arch their backs and leap, or ‘pronk’, they do so by lifting all four legs simultaneously high into the air, yet when horses trot it is the diagonally opposite legs that move together in time.

In vertebrates, gaits are controlled by central pattern generators, which can be thought of as networks of neural oscillators that coordinate output. Depending on the interaction between these oscillators, specific rhythms are produced, which, mathematically speaking, exhibit certain spatiotemporal symmetries. In other words, the gait doesn’t change when one leg is swapped with another – perhaps at a different point in time, say a quarter-cycle or half-cycle later.

It turns out the same symmetries also characterise the swimming gaits of microalgae, which are far too simple to have neurons. For instance, microalgae with four flagella in various possible configurations can trot, pronk or gallop, depending on the species.

“When I peered through the microscope and saw that the alga was performing two sets of perfectly synchronous breaststrokes, one directly after the other, I was amazed,” said the paper’s first author Dr Kirsty Wan of the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the ̽��ֱ�� of Cambridge. “I realised immediately that this behaviour could only be due to something inside the cell rather than passive hydrodynamics. Then of course to prove this I had to expand my species collection.”

̽��ֱ��researchers determined that it is in fact the networks of elastic fibres which connect the flagella deep within the cell that coordinate these diverse gaits. In the simplest case of Chlamydomonas, which swims a breaststroke with two flagella, absence of a particular fibre between the flagella leads to uncoordinated beating. Furthermore, deliberately preventing the beating of one flagellum in an alga with four flagella has zero effect on the sequence of beating in the remainder.

However, this does not mean that hydrodynamics play no role. In recent work from the same group, it was shown that nearby flagella can be synchronised solely by their mutual interaction through the fluid. There is a distinction between unicellular organisms for which good coordination of a few flagella is essential, and multicellular species or tissues that possess a range of cilia and flagella. In the latter case, hydrodynamic interactions are much more important.

“As physicists our instinct is to seek out generalisations and universal principles, but the world of biology often presents us with many fascinating counterexamples,” said Professor Ray Goldstein, Schlumberger Professor of Complex Physical Systems at DAMTP, and senior author of the paper. “Until now there have been many competing theories regarding flagellar synchronisation, but I think we are finally making sense of how these different organisms make best use of what they have.”

̽��ֱ��findings also raise intriguing questions about the evolution of the control of peripheral appendages, which must have arisen in the first instance in these primitive microorganisms.

This research was supported by a Neville Research Fellowship from Magdalene College, and a Senior Investigator Award from the Wellcome Trust.

Reference:
Kirsty Y. Wan and Raymond E. Goldstein. ‘Coordinated beating of algal flagella is mediated by basal coupling.’ PNAS (2016). DOI: 10.1073/pnas.1518527113

Species of single-celled algae use whip-like appendages called flagella to coordinate their movements and achieve a remarkable diversity of swimming gaits.

As physicists our instinct is to seek out generalisations and universal principles, but the world of biology often presents us with many fascinating counterexamples.

Raymond Goldstein

Kirsty Y. Wan & Raymond E. Goldstein

Microscope images showing two species of algae which swim using tiny appendages known as flagella

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

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Killer flies: how brain size affects hunting strategy in the insect world

sc604 — Tue, 09 Feb 2016 09:10:36 +0000

As in economics, there is a law of diminishing returns in neuroscience – doubling the investment going in doesn’t equal double the performance coming out. With a bigger brain comes more available resources that can be allocated to certain tasks, but everything has a cost, and evolution weighs the costs against the benefits in order to make the most efficient system.

“Larger brains are specialised for high performance, so there’s a definite advantage to being bigger and better,” says Professor Simon Laughlin of the Department of Zoology, whose research looks at the cellular costs associated with various neural tasks. “But since most animals actually have very small brains, there must also be advantages to being small.” Indeed, there is strong selection pressure to have the minimum performance required in order to survive and it’s not biologically necessary to be the best, only to be better than the nearest competitor.

So does size matter? Do small insects with relatively few neurons have the same capabilities as much larger animals? “When an animal is limited, is it because their neural system just can’t cope? Or is it because they’re actually optimised for their particular environment?” asks Dr Paloma Gonzalez-Bellido from Cambridge’s Department of Physiology, Development and Neuroscience.

With funding from the US Air Force, Gonzalez-Bellido is studying the hunting behaviours of various flying insects – from tiny killer flies, slightly larger robber flies to large dragonflies – to determine how their visual systems influence their attack strategy, and what sorts of trade-offs they have to make in order to be successful.

Dragonflies are among the largest flying insects, and hunt smaller insects such as mosquitoes while patrolling their territories. They have changed remarkably little in the 300 million years since they evolved – most likely because they are so well optimised for their particular environmental niche.

“Other researchers have found that dragonflies are capable of doing complex things like internally predicting what their body is going to do and compensating for that – for instance, if they’re chasing a target and turn their wings, another signal will be sent to turn their head, so that the target stays in the same spot in their visual field,” says Gonzalez-Bellido. “But are smaller animals, such as tiny flies, capable of achieving similarly complex and accurate feats?”

Gonzalez-Bellido also studies the killer fly, or Coenosia attenuata. These quick and ruthless flies are about four millimetres long, and will go after anything they think they can catch – picky eaters they are not. However, the decision to go after their next meal is not as simple as taking off after whatever tasty-looking morsels happen to fly by. As soon as a killer fly takes off after its potential prey, it exposes itself and runs the risk of becoming a meal for another killer fly.

To help these predacious and cannibalistic flies eat (and prevent them from being eaten), they need to fly fast and to see fast. Insects see at speeds much higher than most other animals, but even for insects, killer flies and dragonflies see incredibly fast, at rates as high as 360 hertz (Hz) – as a comparison, humans see at around 60 Hz.

“For prey animals, the most important thing is to get out of the way quickly – it doesn’t matter whether they know exactly what’s coming, just that it doesn’t catch them,” says Gonzalez-Bellido. “Predators need to be both fast and accurate in their movements if they’re going to be successful – but for very small predators such as insects, there are trade-offs that need to be made.”

By making the ‘pixels’ on their photoreceptors (the light-sensitive cells in the retina) as narrow as possible, killer flies trade sensitivity for resolution. In bright light, they see better than their similar-sized prey, the common fruit fly. However, the cap on sensitivity and resolution imposed upon killer flies by their tiny eyes means that they can only see and attack things that fly close by.

While dragonflies, with their larger eyes and better resolution, can take their time and use their brain power to calculate whether a prey is suitable for an attack, killer flies attack before they’ve had a chance to determine whether it’s something they can actually catch, subdue or eat – or they risk missing their prey altogether. Once a killer fly gets relatively close to its potential prey, it has to decide whether to keep going or turn back – this is one of the trade-offs resulting from evolving such a tiny visual system.

In the early 2000s, Laughlin determined the energy efficiency of single neurons, by estimating the numbers of ATP molecules – the molecules that deliver energy in cells – used per bit of information coded. To do this he compared photoreceptors in various insects. Laughlin and his colleagues found that photoreceptors are like cars – the higher the performance, the more energy they require, and costs rise out of proportion with performance. “For any system, whether it’s in a tiny insect or a large mammal, you don’t want something which is over-engineered, because it’s going to cost more,” says Laughlin. “So what’s the root of inefficiency, and how did nature evolve efficient nerve cells from the bottom up?”

Researchers in the Department of Engineering are taking the reverse approach to answer questions about how the brain works so efficiently by looking at systems from the top down. “If you reverse engineer an animal’s behavioural strategy by asking how an animal would solve a task under specific constraints and then work out the optimal solution, you’ll find it’s often the case that animals are pretty close to optimal,” says Dr Guillaume Hennequin, who looks at how neurons work together to produce behaviour.

Hennequin studies how brain circuits are wired in such a way that they become optimised for a task: how primates such as monkeys are able to estimate the direction of a moving object, for example. “How brain circuits generate optimal interpretations of ambiguous information received from imperfect sensors is still not known,” he says. “Coping with uncertainty is one of the core challenges that brains must confront.”

Different animals come up with their own solutions. Both dragonflies and killer flies have systems that are optimal, but optimal in their own ways. It’s beneficial for killer flies to be so small, since this gives them high manoeuvrability, enabling them to catch prey that turns at speed. Dragonflies are much bigger, and can do things that killer flies can’t, but their size means they can’t turn or stop on a dime, like a killer fly can.

“By answering some of the questions around efficiency in brain circuits, large or small, we may be able to understand fundamental principles about how brains work and how they evolved,” says Laughlin.

Inset images: top to bottom: robber fly, dragon fly, killer fly; credit: Sam Fabian.

Cambridge researchers are studying what makes a brain efficient and how that affects behaviour in insects.

When an animal is limited, is it because their neural system just can’t cope? Or is it because they’re actually optimised for their particular environment?

Paloma Gonzalez-Bellido

Sam Fabian

Size comparison of robber fly, dragon fly, killer fly (left to right)

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

̽��ֱ��man with the golden brain

bjb42 — Tue, 13 Dec 2011 08:32:24 +0000

̽��ֱ��sea squirt, a type of marine filter feeder, swims around looking for somewhere to settle down for the rest of its life. Once parked on a rock in a suitable spot, it never moves again. So the first thing it does is eat its own brain. While this may seem a little rash to some, for Professor Daniel Wolpert it makes perfect evolutionary sense.

“To me it’s obvious that there’s no point in the brain processing or storing anything if it can’t have benefits for physical movement, because that’s the only way we improve our survival,” says Wolpert. “I believe that to understand movement is to understand the whole brain. Memory, cognition, sensory processing – they are there for a reason, and that reason is action.”

Wolpert is firmly convinced that movement is the underlying factor and final result behind every functional aspect of a brain. “There can be no evolutionary advantage to laying down memories of childhood, or perceiving the colour of a rose, if it doesn’t affect the way you’re going to move in later life,” he says.

A professor in the Department of Engineering, Wolpert examines computational models and uses simple behavioural experiments to describe and predict how the brain solves problems related to action. Through this combination of theoretical and behavioural work, Wolpert has begun to revolutionise the study of human sensorimotor control, the way in which the brain controls physical movement.

He was recently presented with the prestigious Golden Brain Award by the California-based Minerva Foundation. ̽��ֱ��award is given to those producing original and outstanding research into the nature of the brain, regarded by many as the most complex object in the known universe.

So what occurs in the brain when humans produce movement? Science has long struggled with the mysteries of this question. Wolpert uses the example of the game of chess: “We have computers that can generate algorithms of possible chess moves at tremendous speeds, beating the best human chess players. But ask a machine to compete on a dextrous level, such as moving a chess piece from one square to another, and the most advanced robot will fail every time against the average five-year-old child.”

̽��ֱ��models employed by Wolpert and his team have yielded startling results, offering a possible glimpse into the patterns integral to our mental matrix. “It turns out that the brain behaves in a very statistical manner, representing information about the world as probabilities and processes, which is possible to predict mathematically,” says Wolpert. “We’ve shown that this is a very powerful framework for understanding the brain.”

For action to occur, a command is sent from the brain causing muscles to contract and the body to move. Sensory feedback is then received from vision, skin, muscles and so on, to help gauge success. Sounds simple, but a vast amount of misinformation or ‘noise’ is generated with even the most basic action, due to the imperfections in our senses and the almost incalculable variables of the physical world around us. “We work in a whole sensory/task soup of noise,” says Wolpert. “ ̽��ֱ��brain goes to a lot of effort to reduce the negative consequences of this noise and variability.”

̽��ֱ��brain’s crystal ball

To combat this noise, our brains have developed a sophisticated predictive ability, so that every action is based on an orchestrated balance between current sensory data and, crucially, past experience. Memory is a key factor in allowing the brain to make the optimal ‘best guess’ for cutting through the noise, producing the most advantageous movement for the task. In this way, our brains are constantly attempting to predict the future.

“An intuitive example of this predictive ability might be returning a serve in tennis. You need to decide where the ball is going to bounce to produce the most effective return. ̽��ֱ��brain uses the sensory evidence, such as vision and sound, and combines it with experience, prior knowledge of where the ball has bounced in the past. This creates an area of ‘belief’, the brains best guess of where ball will hit court, and the command for action is generated accordingly.”

Movement can take a long time from command to muscles, which can leave us exposed. Like chess, we need to be anticipating several moves ahead, so the brain uses its predictive ability to try and internally replicate the response to an action as or even before it is made, a kind of inbuilt simulator. ̽��ֱ��brain then subtracts this simulation from our actual experience, so it isn’t adding to the noise of misinformation.

“For behavioural causality, we need to be more attuned to the outside world as opposed to inside our own bodies. When our neural simulator makes a prediction, it is only based on internal movement commands. ̽��ֱ��brain subtracts that prediction from the overall sensation, so that everything left over is hopefully external.”

But this can have intriguing effects on our perceptions of the physical world, and the consequences of our actions. “This is why we can’t tickle ourselves, as tickling relies on an inability to predict sensation, and your neural simulator has already subtracted the sensation from the signal,” says Wolpert.

“But they hit me harder!”

A further example of this sensory subtraction occurred to Wolpert during a

backseat bust-up between his daughters, a familiar experience for most parents during long car journeys. ̽��ֱ��traditional escalation of hostility was ensuing as each child claimed they got hit harder and so retaliated in kind.

Wolpert explains: “You underestimate a force when you generate it, so as one child hits another, they predict the sensory movement consequences and subtract it off, thinking they’ve hit the other less hard than they have. Whereas the recipient doesn’t make the prediction so feels the full blow. So if they retaliate with the same force, it will appear to the first child to have been escalated.”

This observation led to a simple but effective experiment being conducted called ‘tit for tat’, in which two adults sit opposite each other with their fingers on either side of a force transducer. They were asked to replicate the force demonstrated by each other when pushing against the other's finger. Instead of remaining constant, a 70 percent escalation of force is recorded on each go. It seems that we really don’t know our own strength.

Deciding to act

̽��ֱ��next challenge for Wolpert is to investigate how we make the decision to act, and what happens in the brain if we change our minds after the initial decision. “We think that the fields of both decision making and action share a lot of common features, and our goal is to try and link them together to create a unifying model of how actions affect decisions and vice versa,” says Wolpert.

“As we walk around the world, do our decisions depend on how much effort is required, and to what extend does perceived effort influence the decisions we make? Similarly, to what extent does perceived effort relate to the decision to change our minds? These are the questions we want to address.”

To this end, Wolpert is about to begin on a project for the Human Frontiers Science Programme on linking decision to action. “We’ve developed robotic interfaces in the lab which allow us to control and create experiences that people won’t have had before,” he says.

“We ask subjects to perform simple tasks using a joystick. Once they are in a rhythm, we generate forces that act proportionally to speed but perturb their arm in unusual ways, such as right angles, and see how they respond. This allows us to build a dataset on novel learning, how people adapt to various forces, and the decisions that they make in the process.”

Wolpert’s ultimate aim is to apply these models of the brain and how it controls movement to a greater understanding of brain disorders. As he explains: “Five percent of the population suffers from diseases that affect movement. ̽��ֱ��hope is that we will not only understand what goes wrong in disease, but how to design better mechanisms for rehabilitation.”

What’s the point of a brain? This fundamental question has led Professor Daniel Wolpert to some remarkable conclusions about how and why the brain controls and predicts movement. In a recent talk for TED, Wolpert explores the research that resulted in him receiving the Golden Brain Award.

I believe that to understand movement is to understand the whole brain.

Professor Daniel Wolpert

Daniel Wolpert: ̽��ֱ��real reason for brains

thelunch_box via Flickr

Neurons, in vitrio colour!

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