Periodic Reporting for period 4 - EMERG-ANT (Ant navigation: how complex behaviours emerge from mini-brains in interaction with their natural habitats)
Okres sprawozdawczy: 2022-07-01 do 2024-06-30
A fundamental endeavour of science is to understand how brains produce complex behaviours in the wild. Behaviour is the fruit of not only the brain, but also the body, and the natural environment in which the animal has evolved. Insects provide a uniquely powerful system to investigate because: 1- they display exquisitely sophisticated behaviours, particularly when it comes to navigation; 2- they do so with a nervous system numerically much simpler than vertebrates, and 3- their behaviour can be studied directly in their natural environment. However, despite considerable advances in our understanding of both the natural behaviour (studied in the field) and the neurobiology of insects (studied in the lab), we are still far from understanding how behaviours emerge from the interaction between brain, body and environment.
The project brings the field and the lab together by using a new experimental tool enabling the full control of the sensory-motor experience of ants as they navigate in virtual-reality reconstructions of their natural environments. This tool enables us to manipulate the virtual world in any possible way while the ant displays their natural navigation task, opening the door to new experimentations that cannot possibly be tackled in the real world. We seek to characterise 1- how insects encode complex natural scenes, 2- how they integrate multiple sources of information, 3- how they store and combine visuo-motor memories and 4- what are the rules underlying their motor control.
Also, our results are systematically interpreted in the light of the insect brain circuits. All our hypotheses are implemented as neural models embedded in simulated agents navigating in the same reconstructed virtual environment as the ants. Our agents are subjected to the same manipulations as the ants and the resulting behaviour can directly compare to the ant data. This modelling effort enables us to pinpoint the gaps in our understanding of the mechanisms, as well as make specific predictions, and thus drive our experimental questions.
Insects' brain may be different in scale and shape than vertebrates' brain, but the actual neural computation can be bafflingly similar, suggesting that understanding one can help understand another. Studying navigation has another advantage: going from A to B without getting lost is a task shared by most animals, including humans. Therefore, this project can help to identify universal neural rules which underlie also our own behaviours.
The project brings the field and the lab together by using a new experimental tool enabling the full control of the sensory-motor experience of ants as they navigate in virtual-reality reconstructions of their natural environments. This tool enables us to manipulate the virtual world in any possible way while the ant displays their natural navigation task, opening the door to new experimentations that cannot possibly be tackled in the real world. We seek to characterise 1- how insects encode complex natural scenes, 2- how they integrate multiple sources of information, 3- how they store and combine visuo-motor memories and 4- what are the rules underlying their motor control.
Also, our results are systematically interpreted in the light of the insect brain circuits. All our hypotheses are implemented as neural models embedded in simulated agents navigating in the same reconstructed virtual environment as the ants. Our agents are subjected to the same manipulations as the ants and the resulting behaviour can directly compare to the ant data. This modelling effort enables us to pinpoint the gaps in our understanding of the mechanisms, as well as make specific predictions, and thus drive our experimental questions.
Insects' brain may be different in scale and shape than vertebrates' brain, but the actual neural computation can be bafflingly similar, suggesting that understanding one can help understand another. Studying navigation has another advantage: going from A to B without getting lost is a task shared by most animals, including humans. Therefore, this project can help to identify universal neural rules which underlie also our own behaviours.
We developed a system to attach ants on top of an air floating ball, acting like a treadmill. The ants are remarkably at ease on the setup, whether directly in the field or in the lab virtual reality system: they maintain their motivation to navigate and display their usual motor behaviours, as if undisturbed. Field experiments using these trackball systems enabled us to ask novel questions to dig into their mechanisms. For instance, we reveal that, contrary to previous theories, ants could recognise their route no matter the direction they faced, and could infer from this situation which side was the correct goal direction. Following on this, we showed that learning is not something that happen during a specific moment, such as when encountering a reward or looking at the goal, but happens continuously, echoing the so-called ‘latent learning’ in vertebrates. Also, we revealed that navigating ants are using a two-step strategies: visual recognition of the scene is not used to drive motor behaviour directly, but this information updates a neural representation of the goal direction, based on celestial cues, which they then use to guide their movements.
Our modelling effort revealed how the insects brain circuitry could implement these mechanisms. We revealed how simple neural process in the insect early visual system could enable to strongly improve the recognition of complex natural scenes, which happen deeper in the brain. This compression of the visual information explains how ants can learn continuously while navigating over thousands of square meters, without memory saturation. Further experiments in the field revealed how ants learn aversive memories to avoid regions associated with danger, and our neural models show how such aversive memories can be combined with appetitive memories during navigation. When embodied in a simulated agent navigating in reconstructed worlds, our neural models now achieves amazingly robust navigation!
Regarding the motor aspect of the ant’s navigation skills, we revealed the existence of an intrinsic oscillator at the core of their navigation system, and located in an ancestral pre-motor area of their brain. We show that multiple pathways from different modalities converge onto this oscillator, which provides a natural way of integrating them while maintaining a smooth and efficient behavioural output.
In parallel, we developed a Virtual Reality system based on LED’s, to project reconstructed natural worlds on a cylindrical screen, in the centre of which the ant is navigating on its treadmill. This setup enable to manipulate the scene while the ant is navigating. For instance, by reversing the relationship between the ant’s movement and the visual feedback, we showed that ants compute predictions of the motion of the scene they expect based on their own movements. They do not adjust their locomotion using the perceived motion, but using a prediction error, that is, the mismatch between their actual perception and prediction, like vertebrates’ brain do. What’s more, the way ants make prediction is not trivial: they combine information from their motor commands, their proprioception as well as the appearance of the scene (some scene ‘should’ produce more optic-flow when turning than others, and ants take this into account).
Here again, neural modelling enabled us to explore how these mechanisms can be implemented in the insects’ brain. Notably, we showed how neural feedbacks from motor commands, intrinsic oscillator, and learning centres can interact to orchestrate the continuous formation and retrieval of the multiple parallel memories used to produce a robust navigation behaviour.
Our modelling effort revealed how the insects brain circuitry could implement these mechanisms. We revealed how simple neural process in the insect early visual system could enable to strongly improve the recognition of complex natural scenes, which happen deeper in the brain. This compression of the visual information explains how ants can learn continuously while navigating over thousands of square meters, without memory saturation. Further experiments in the field revealed how ants learn aversive memories to avoid regions associated with danger, and our neural models show how such aversive memories can be combined with appetitive memories during navigation. When embodied in a simulated agent navigating in reconstructed worlds, our neural models now achieves amazingly robust navigation!
Regarding the motor aspect of the ant’s navigation skills, we revealed the existence of an intrinsic oscillator at the core of their navigation system, and located in an ancestral pre-motor area of their brain. We show that multiple pathways from different modalities converge onto this oscillator, which provides a natural way of integrating them while maintaining a smooth and efficient behavioural output.
In parallel, we developed a Virtual Reality system based on LED’s, to project reconstructed natural worlds on a cylindrical screen, in the centre of which the ant is navigating on its treadmill. This setup enable to manipulate the scene while the ant is navigating. For instance, by reversing the relationship between the ant’s movement and the visual feedback, we showed that ants compute predictions of the motion of the scene they expect based on their own movements. They do not adjust their locomotion using the perceived motion, but using a prediction error, that is, the mismatch between their actual perception and prediction, like vertebrates’ brain do. What’s more, the way ants make prediction is not trivial: they combine information from their motor commands, their proprioception as well as the appearance of the scene (some scene ‘should’ produce more optic-flow when turning than others, and ants take this into account).
Here again, neural modelling enabled us to explore how these mechanisms can be implemented in the insects’ brain. Notably, we showed how neural feedbacks from motor commands, intrinsic oscillator, and learning centres can interact to orchestrate the continuous formation and retrieval of the multiple parallel memories used to produce a robust navigation behaviour.
Altogether, our work shows how a distributed brain architecture, with independent modules (perception, memory formation, compass processing, internal oscillators, motor commands) reverberating as a closed-loop process through multiple bidirectional feedbacks, can produce the emergence of robust navigation behaviour when embodied in moving agents.
Our results had strong implication for the mechanisms underlying insect navigation and motor control: they refuted previous models, and pushed us to understand how their neural circuits can underlie feats that were not expected in insects, and sometimes echoes what is found in human literature, such as making prediction or learning continuously, and shedding light on the differences and similarities between invertebrate and vertebrate cognition.
Finally, some results were fully unexpected, such as the spontaneous ability of ants to compensate after a strong sensory motor defect, here again echoing human abilities. This revealed the need for a change of paradigm to understand insect brains: neural models should add a deeper level of plasticity to address the self-developmental nature and resilience of organisms. This now motivates my future research.
Our results had strong implication for the mechanisms underlying insect navigation and motor control: they refuted previous models, and pushed us to understand how their neural circuits can underlie feats that were not expected in insects, and sometimes echoes what is found in human literature, such as making prediction or learning continuously, and shedding light on the differences and similarities between invertebrate and vertebrate cognition.
Finally, some results were fully unexpected, such as the spontaneous ability of ants to compensate after a strong sensory motor defect, here again echoing human abilities. This revealed the need for a change of paradigm to understand insect brains: neural models should add a deeper level of plasticity to address the self-developmental nature and resilience of organisms. This now motivates my future research.