̽��ֱ�� of Cambridge - Guillaume Hennequin

What’s going on in our brains when we plan?

sc604 — Tue, 11 Jun 2024 09:46:21 +0000

In pausing to think before making an important decision, we may imagine the potential outcomes of different choices we could make. While this ‘mental simulation’ is central to how we plan and make decisions in everyday life, how the brain works to accomplish this is not well understood.

An international team of scientists has now uncovered neural mechanisms used in planning. Their results, published in the journal Nature Neuroscience, suggest that an interplay between the brain’s prefrontal cortex and hippocampus allows us to imagine future outcomes to guide our decisions.

“ ̽��ֱ��prefrontal cortex acts as a ‘simulator,’ mentally testing out possible actions using a cognitive map stored in the hippocampus,” said co-author Marcelo Mattar from New York ̽��ֱ��. “This research sheds light on the neural and cognitive mechanisms of planning—a core component of human and animal intelligence. A deeper understanding of these brain mechanisms could ultimately improve the treatment of disorders affecting decision-making abilities.”

̽��ֱ��roles of both the prefrontal cortex—used in planning and decision-making—and hippocampus—used in memory formation and storage—have long been established. However, their specific duties in deliberative decision-making, which are the types of decisions that require us to think before acting, are less clear.

To illuminate the neural mechanisms of planning, Mattar and his colleagues—Kristopher Jensen from ̽��ֱ�� College London and Professor Guillaume Hennequin from Cambridge’s Department of Engineering —developed a computational model to predict brain activity during planning. They then analysed data from both humans and rats to confirm the validity of the model—a recurrent neural network (RNN), which learns patterns based on incoming information.

̽��ֱ��model took into account existing knowledge of planning and added new layers of complexity, including ‘imagined actions,’ thereby capturing how decision-making involves weighing the impact of potential choices—similar to how a chess player envisions sequences of moves before committing to one. These mental simulations of potential futures, modelled as interactions between the prefrontal cortex and hippocampus, enable us to rapidly adapt to new environments, such as taking a detour after finding a road is blocked.

̽��ֱ��scientists validated this computational model using both behavioural and neural data. To assess the model’s ability to predict behaviour, the scientists conducted an experiment measuring how humans navigated an online maze on a computer screen and how long they had to think before each step.

To validate the model’s predictions about the role of the hippocampus in planning, they analysed neural recordings from rodents navigating a physical maze configured in the same way as in the human experiment. By giving a similar task to humans and rats, the researchers could draw parallels between the behavioural and neural data—an innovative aspect of this research.

“Allowing neural networks to decide for themselves when to 'pause and think' was a great idea, and it was surprising to see that in situations where humans spend time pondering what to do next, so do these neural networks,” said Hennequin.

̽��ֱ��experimental results were consistent with the computational model, showing an intricate interaction between the prefrontal cortex and hippocampus. In the human experiments, participants’ brain activity reflected more time thinking before acting in navigating the maze. In the experiments with laboratory rats, the animals’ neural responses in moving through the maze resembled the model’s simulations.

“Overall, this work provides foundational knowledge on how these brain circuits enable us to think before we act in order to make better decisions,” said Mattar. “In addition, a method in which both human and animal experimental participants and RNNs were all trained to perform the same task offers an innovative and foundational way to gain insights into behaviours.”

“This new framework will enable systematic studies of thinking at the neural level,” said Hennequin. “This will require a concerted effort from neurophysiologists and theorists, and I'm excited about the discoveries that lie ahead.”

Reference:
Kristopher T. Jensen, Guillaume Hennequin & Marcelo G. Mattar. ‘A recurrent network model of planning explains hippocampal replay and human behavior.’ Nature Neuroscience (2024). DOI: 10.1038/s41593-024-01675-7

Adapted from an NYU press release.

Study uncovers how the brain simulates possible future actions by drawing from our stored memories.

Andriy Onufriyenko via Getty Images

Metaverse portrait

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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.

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