Learning to Look and Looking to Remember: A Neural-Dynamic Embodied Model for Generation of Saccadic Gaze Shifts and Memory Formation

Sandamirskaya, Yulia; Storck, Tobias

doi:10.1007/978-3-319-09903-3_9

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…Understanding how relational concepts may be learned from experience (Doumas et al., 2008) is the goal of an entire research program (Samuelson & Faubel, 2016; Samuelson, Kucker, & Spencer, 2017), to which the neural learning mechanisms of DFT may provide an entry point (Sandamirskaya, 2014; Sandamirskaya & Storck, 2015). In the present model, however, all synaptic connectivity was fixed.…”

Section: Discussionmentioning

confidence: 99%

A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations

2021

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How does the human brain link relational concepts to perceptual experience? For example, a speaker may say "the cup to the left of the computer" to direct the listener's attention to one of two cups on a desk. We provide a neural dynamic account for both perceptual grounding, in which relational concepts enable the attentional selection of objects in the visual array, and for the generation of descriptions of the visual array using relational concepts. In the model, activation in neural populations evolves dynamically under the influence of both inputs and strong interaction as formalized in dynamic field theory. Relational concepts are modeled as patterns of connectivity to perceptual representations. These generalize across the visual array through active coordinate transforms that center the representation of target objects in potential reference objects. How the model perceptually grounds or generates relational descriptions is probed in 104 simulations that systematically vary the spatial and movement relations employed, the number of feature dimensions used, and the number of matching and nonmatching objects. We explain how sequences of decisions emerge from the time-and state-continuous neural dynamics, how relational hypotheses are generated and either accepted or rejected, followed by the selection of new objects or the generation of new relational hypotheses. Its neural realism distinguishes the model from information processing accounts, its capacity to autonomously generate sequences of processing steps distinguishes it from deep neural network accounts. The model points toward a neural dynamic theory of higher cognition.

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Section: Discussionmentioning

confidence: 99%

A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations

2021

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“…In robotics, reinforcement learning is used as a paradigm to learn from experience, but not typically within neurally grounded architectures [for review, see, Kormushev et al (2013)]. DFT provides the processing infrastructure that supports autonomous learning from experience (Sandamirskaya, 2014; Sandamirskaya and Storck, 2015). For instance, neural states that drive exploratory behavior must be kept in working memory to compare with the outcome.…”

Section: Discussionmentioning

confidence: 99%

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

et al. 2016

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Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

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“…The latter two are adaptations of standard mechanisms, which were developed in neural models of movement generation [19], other elements may be mapped on different parts of cortical and subcortical structures (e.g., superior calliculus for saccade target representation, or cerebellum for gain maps adaptation), involved in saccade generation, as we discussed recently [20].…”

Section: Methods: Dynamic Neural Fieldsmentioning

confidence: 99%

Neural-dynamic architecture for looking: Shift from visual to motor target representation for memory saccades

Sandamirskaya

Storck

2014

4th International Conference on Development and Learning and on Epigenetic Robotics

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Looking at objects is one of the most basic sensorimotor behaviours, which requires calibration of the perceptual and motor systems. Recently, we have introduced a neural-dynamic architecture, in which the sensorimotor transformations, which lead to precise saccadic gaze shifts, is initially learned and is autonomously updated if changes in the environment or in the motor plant of the agent require adaptation. Here, we demonstrate how the allocentric, gazedirection independent memory representations may be formed in this architecture and how sequences of precise gaze shifts may be generated to memorised targets. Our simulated robotic experiments demonstrate functioning of the architecture on an autonomous embodied agent.

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Learning to Look and Looking to Remember: A Neural-Dynamic Embodied Model for Generation of Saccadic Gaze Shifts and Memory Formation

Cited by 10 publications

References 37 publications

A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations

A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

Neural-dynamic architecture for looking: Shift from visual to motor target representation for memory saccades

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