Simulating lesion-dependent functional recovery mechanisms

Sajid, Noor; Holmes, Emma; Hope, Thomas M.H.; Fountas, Zafeirios; Price, Cathy J.; Friston, Karl J.

doi:10.1038/s41598-021-87005-4

Cited by 7 publications

(3 citation statements)

References 63 publications

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“…Putatively, the combination of overexcitable FPl and stronger amygdala afferences when controlling emotional actions we observe in high-anxious, might make it difficult for anxious individuals to maintain their private sense of confidence in their opinions when conforming to social norms, a role attributed to FPl 41 . Furthermore, given degeneracy in emotionrelated neural circuits 42 , the effects of a saturated FPl tuning might alter those circuits, inducing other prefrontal control nodes to take over FPl computational contributions [43][44][45] . Here, we show that even a mild emotion-regulation challenge results in an anxiety-related shift from FPl to dlPFC, while preserving behavioral performance.…”

Section: Discussionmentioning

confidence: 99%

Anxious individuals shift emotion control from lateral frontal pole to dorsolateral prefrontal cortex

et al. 2023

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Anxious individuals consistently fail in controlling emotional behavior, leading to excessive avoidance, a trait that prevents learning through exposure. Although the origin of this failure is unclear, one candidate system involves control of emotional actions, coordinated through lateral frontopolar cortex (FPl) via amygdala and sensorimotor connections. Using structural, functional, and neurochemical evidence, we show how FPl-based emotional action control fails in highly-anxious individuals. Their FPl is overexcitable, as indexed by GABA/glutamate ratio at rest, and receives stronger amygdalofugal projections than non-anxious male participants. Yet, high-anxious individuals fail to recruit FPl during emotional action control, relying instead on dorsolateral and medial prefrontal areas. This functional anatomical shift is proportional to FPl excitability and amygdalofugal projections strength. The findings characterize circuit-level vulnerabilities in anxious individuals, showing that even mild emotional challenges can saturate FPl neural range, leading to a neural bottleneck in the control of emotional action tendencies.

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Section: Discussionmentioning

confidence: 99%

Anxious individuals shift emotion control from lateral frontal pole to dorsolateral prefrontal cortex

et al. 2023

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“…We expect that replacing our Q-learning planner, with more sophisticated objectives equipped to handle aleatoric and epistemic uncertainties (e.g., expected free energy [45][46][47] ), could improve the performance of the robot when dealing with volatile contingencies 46 . Furthermore, the robustness of our method could be evaluated through robotic neuropsychology 48 , i.e., introducing in-silico lesions to the robot system, and investigating their effect on the resulting policies, inference and behaviour.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical generative modelling for autonomous robots

Yuan¹,

Sajid²,

Friston³

et al. 2021

Preprint

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Humans can produce complex movements when interacting with their surroundings. This relies on the planning of various movements and subsequent execution. In this paper, we investigated this fundamental aspect of motor control in the setting of autonomous robotic operations. We consider hierarchical generative modelling—for autonomous task completion—that mimics the deep temporal architecture of human motor control. Here, temporal depth refers to the nested time scales at which successive levels of a forward or generative model unfold: for example, the apprehension and delivery of an object requires both a global plan that contextualises the fast coordination of multiple local limb movements. This separation of temporal scales can also be motivated from a robotics and control perspective. Specifically, to ensure versatile sensorimotor control, it is necessary to hierarchically structure high-level planning and low-level motor control of individual limbs. We use numerical experiments to establish the efficacy of this formulation and demonstrate how a humanoid robot can autonomously solve a complex task requiring locomotion, manipulation, and grasping, using a hierarchical generative model. In particular, the humanoid robot can retrieve and deliver a box, open and walk through a door to reach the final destination. Our approach, and experiments, illustrate the effectiveness of using human-inspired motor control algorithms, which provide a scalable hierarchical architecture for autonomous performance of complex goal-directed tasks.

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“…Furthermore, robustness can be evaluated through robotic neuropsychology 53 that is, introducing in-silico lesions by perturbing various approximations and policies and investigating their effect on the ensuing inference and behaviour. These computational lesions can be introduced in both simulated and physical robots, where lesions of this sort can change functional outcomes.…”

Section: Future Directionsmentioning

confidence: 99%

Hierarchical generative modelling for autonomous robots

Yuan,

Sajid,

Friston

et al. 2023

Nat Mach Intell

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Humans generate intricate whole-body motions by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control and approached the problem of autonomous task completion by hierarchical generative modelling with multi-level planning, emulating the deep temporal architecture of human motor control. We explored the temporal depth of nested timescales, where successive levels of a forward or generative model unfold, for example, object delivery requires both global planning and local coordination of limb movements. This separation of temporal scales suggests the advantage of hierarchically organizing the global planning and local control of individual limbs. We validated our proposed formulation extensively through physics simulation. Using a hierarchical generative model, we showcase that an embodied artificial intelligence system, a humanoid robot, can autonomously complete a complex task requiring a holistic use of locomotion, manipulation and grasping: the robot adeptly retrieves and transports a box, opens and walks through a door, kicks a football and exhibits robust performance even in the presence of body damage and ground irregularities. Our findings demonstrated the efficacy and feasibility of human-inspired motor control for an embodied artificial intelligence robot, highlighting the viability of the formulized hierarchical architecture for achieving autonomous completion of challenging goal-directed tasks.

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Simulating lesion-dependent functional recovery mechanisms

Cited by 7 publications

References 63 publications

Anxious individuals shift emotion control from lateral frontal pole to dorsolateral prefrontal cortex

Anxious individuals shift emotion control from lateral frontal pole to dorsolateral prefrontal cortex

Hierarchical generative modelling for autonomous robots

Hierarchical generative modelling for autonomous robots

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