Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task

Rajalingham, Rishi; Piccato, Aída; Jazayeri, Mehrdad

doi:10.1038/s41467-022-33581-6

Cited by 24 publications

(9 citation statements)

References 62 publications

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“…A critical step for bridging insights between human and monkey behavior is through the computational approach that could explain behavior equally well in both human and monkey performance (Badre et al, 2015;Rajalingham et al, 2022). In an earlier attempt of modeling CST, a simple PD controller with delay in sensory feedback was proposed to explain the recorded behavior (Quick et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…To achieve this objective, cooperative study designs between human and animal motor research are needed to understand the neural basis of human motor skill (Badre et al, 2015;Rajalingham et al, 2022). However, there are difficult challenges to overcome: First, cooperative design requires matching behavioral tasks that can be performed similarly and with the same conditions by both humans and animals.…”

Section: Introductionmentioning

confidence: 99%

“…To achieve this goal, cooperative study designs between human and animal motor research are needed to understand the neural basis of human motor skill (Badre et al, 2015;Rajalingham et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Inferring control objectives in a virtual balancing task in humans and monkeys

Sadeghi

Razavian²,

Bazzi

et al. 2023

Preprint

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Natural behaviors have redundancy, which implies that humans and animals can achieve their goals with different control strategies. Given only observations of behavior, is it possible to infer the control strategy that the subject is employing? This challenge is particularly acute in animal behavior because we cannot ask or instruct the subject to use a particular control strategy. This study presents a three-pronged approach to infer an animals control strategy from behavior. First, both humans and monkeys performed a virtual balancing task for which different control strategies could be utilized. Under matched experimental conditions, corresponding behaviors were observed in humans and monkeys. Second, a generative model was developed that identified two main control strategies to achieve the task goal. Model simulations were used to identify aspects of behavior that could distinguish which control strategy was being used. Third, these behavioral signatures allowed us to infer the control strategy used by human subjects who had been instructed to use one control strategy or the other. Based on this validation, we could then infer strategies from animal subjects. Being able to positively identify a subjects control strategy from behavior can provide a powerful tool to neurophysiologists as they seek the neural mechanisms of sensorimotor coordination.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring control objectives in a virtual balancing task in humans and monkeys

Sadeghi

Razavian²,

Bazzi

et al. 2023

Preprint

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“…There has recently been significant interest in RNNs as surrogate models for the computations performed by the brain 13,[44][45][46] . Previous approaches for comparing computations in RNNs for a given task relied on static representations of population activity snapshots 44,47,48 .…”

Section: Discriminating Computational Mechanisms In Recurrent Neural ...mentioning

confidence: 99%

Interpretable statistical representations of neural population dynamics and geometry

Peach

Arnaudon

Barahona

et al. 2023

Preprint

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The dynamics of neuron populations during diverse behaviours evolve on low-dimensional manifolds. However, it remains challenging to disentangle the role of manifold geometry and dynamics in encoding task variables. Here, we introduce an unsupervised geometric deep learning framework for representing non-linear dynamical systems based on statistical distributions of local dynamical features. Our method provides geometry-aware or geometry-agnostic representations for robustly comparing dynamical systems based on sparse measurements. Our representations are generalisable to compare computations across systems, interpretable to discover a geometric correspondence between neural dynamics and kinematics in a primate reaching task, and intrinsically encode temporal information to give rise to a decoding algorithm with state-of-the-art accuracy. Our results suggest that using the manifold structure over temporal information is important to develop better decoding algorithms and assimilate data across experiments.

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“…The absence of research on mental and visual simulation in animals is not surprising, given the complexity, introspection, and subjectivity associated with these phenomena. While some recent evidence suggests that computational models of simulation align with nonhuman primate behavior, it remains unclear whether animals are capable of mental simulation, let alone visual simulation 6 . In our current experiments, we aimed to address these questions by replicating our human studies on visual simulation with nonhuman primates (NHPs).…”

mentioning

confidence: 99%

Monkeys engage in visual simulation to solve complex problems

Ahuja,

Yusif Rodriguez,

Ashok

et al. 2024

Preprint

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Visual simulation — i.e., using internal reconstructions of the world to experience potential future versions of events that are not currently happening — is among the most sophisticated capacities of the human mind. But is this ability in fact uniquely human? To answer this question, we tested monkeys on a series of experiments involving the ‘Planko’ game, which we have previously used to evoke visual simulation in human participants. We found that monkeys were able to successfully play the game using a simulation strategy, predicting the trajectory of a ball through a field of planks while demonstrating a level of accuracy and behavioral signatures comparable to humans. Computational analyses further revealed that the monkeys’ strategy while playing Planko aligned with a recurrent neural network (RNN) that approached the task using a spontaneously learned simulation strategy. Finally, we carried out awake functional magnetic resonance imaging while monkeys played Planko. We found activity in motion-sensitive regions of the monkey brain during hypothesized simulation periods, even without any perceived visual motion cues. This neural result closely mirrors previous findings from human research, suggesting a shared mechanism of visual simulation across species. In all, these findings challenge traditional views of animal cognition, proposing that nonhuman primates possess a complex cognitive landscape, capable of invoking imaginative and predictive mental experiences to solve complex everyday problems.

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Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task

Cited by 24 publications

References 62 publications

Inferring control objectives in a virtual balancing task in humans and monkeys

Inferring control objectives in a virtual balancing task in humans and monkeys

Interpretable statistical representations of neural population dynamics and geometry

Monkeys engage in visual simulation to solve complex problems

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