Navigation in a Space With Moving Objects: Rats Can Avoid Specific Locations Defined With Respect to a Moving Robot

Ahuja, Nikhil; Lobellová, Veronika; Stuchlı́k, Aleš; Kelemen, Eduard

doi:10.3389/fnbeh.2020.576350

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…CA2 receives projections from the basal nucleus of the amygdala, which plays an important role for contextual fear conditioning (Pitkanen et al, 2000; Goosens & Maren, 2001). Ahuja et al (2020) demonstrated that CA1 pyramidal cells were responsive to a robot that indicates a shock zone. Robots have also been developed for the purpose of improving behavioral reproducibility when examining aspects of rodent spatial cognition in neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Interactive Neurorobotics: Behavioral and Neural Dynamics of Agent Interactions

Leonardis

Breston

Lucero-Moore

et al. 2022

Preprint

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Interactive neurorobotics is a subfield which characterizes brain responses evoked during interaction with a robot, and their relationship with the behavioral responses. Gathering rich neural and behavioral data from humans or animals responding to agents can act as a scaffold for the design process of future social robots. The goals of this research can be broadly broken down into two categories. The first, seeks to directly study how organisms respond to artificial agents in contrast to biological or inanimate ones. The second, uses the novel affordances of the robotic platforms to investigate complex phenomena, such as responses to multisensory stimuli during minimally structured interactions, that would be difficult to capture with classical experimental setups. Here we argue that to realize the full potential of the approach, both goals must be integrated through methodological design that is informed by a deep understanding of the model system, as well as engineering and analytical considerations. We then propose a general framework for such experiments that emphasizes naturalistic interactions combined with multimodal observations and complementary analysis pipelines that are necessary to render a holistic picture of the data for the purpose of informing robotic design principles. Finally, we demonstrate this approach with an exemplar rat-robot social interaction task which included simultaneous multi-agent tracking and neural recordings.

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Section: Introductionmentioning

confidence: 99%

Interactive Neurorobotics: Behavioral and Neural Dynamics of Agent Interactions

Leonardis

Breston

Lucero-Moore

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…CA2 receives projections from the basal nucleus of the amygdala, which plays an important role for contextual fear conditioning ( Pitkänen et al, 2000 ; Goosens and Maren, 2001 ). Ahuja et al (2020) demonstrated that CA1 pyramidal cells were responsive to a robot that indicates a shock zone. Robots have also been developed for the purpose of improving behavioral reproducibility when examining aspects of rodent spatial cognition in neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Interactive neurorobotics: Behavioral and neural dynamics of agent interactions

et al. 2022

View full text Add to dashboard Cite

Interactive neurorobotics is a subfield which characterizes brain responses evoked during interaction with a robot, and their relationship with the behavioral responses. Gathering rich neural and behavioral data from humans or animals responding to agents can act as a scaffold for the design process of future social robots. This research seeks to study how organisms respond to artificial agents in contrast to biological or inanimate ones. This experiment uses the novel affordances of the robotic platforms to investigate complex dynamics during minimally structured interactions that would be difficult to capture with classical experimental setups. We then propose a general framework for such experiments that emphasizes naturalistic interactions combined with multimodal observations and complementary analysis pipelines that are necessary to render a holistic picture of the data for the purpose of informing robotic design principles. Finally, we demonstrate this approach with an exemplar rat–robot social interaction task which included simultaneous multi-agent tracking and neural recordings.

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“…These RNN models [27,28], when trained on single agent path integration, have been shown to develop properties analogous to MEC. In particular, they exhibit units with grid, border, and spatially periodic "band" responses [27][28][29][30] and population dynamics consistent with continuous toroidal attractors [31,32]. While their biological plausibility remains unclear [33,34], we choose to analyze these RNN models for three reasons.…”

mentioning

confidence: 99%

Not so griddy: Internal representations of RNNs path integrating more than one agent

Redman,

Acosta,

Acosta–Mendoza

et al. 2024

Preprint

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Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self’s position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses, stronger border responses, and tuning for therelativeposition of the two agents. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation.

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Navigation in a Space With Moving Objects: Rats Can Avoid Specific Locations Defined With Respect to a Moving Robot

Cited by 6 publications

References 41 publications

Interactive Neurorobotics: Behavioral and Neural Dynamics of Agent Interactions

Interactive Neurorobotics: Behavioral and Neural Dynamics of Agent Interactions

Interactive neurorobotics: Behavioral and neural dynamics of agent interactions

Not so griddy: Internal representations of RNNs path integrating more than one agent

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