Static internal representation of dynamic situations reveals time compaction in human cognition

Villacorta-Atienza, José Antonio; Tapia, Carlos Calvo; Díez-Hermano, Sergio; Sánchez‐Jiménez, Abel; Lobov, Sergey; Krilova, Nadia; Murciano, Antonio; López-Tolsa, Gabriela E.; Pellón, Ricardo; Makarov, Valeri A.

doi:10.1016/j.jare.2020.08.008

Cited by 10 publications

(6 citation statements)

References 71 publications

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“…Second, our results suggest that rats can effectively use the time compaction mechanism to internally represent dynamic spatial situations as static maps capturing predicted object interactions. This work supports the encoding of future interactions as an efficient mechanism of spatiotemporal cognition, suggesting it as an evolutionary invariant after similar phenomena are reported in humans 21 and bats 29 . Moreover, spatiotemporal processing is essential for episodic memory 30 , and its impairment is a precursor to cognitive decline and has consequences for various neuropsychiatric illnesses, including dementia and schizophrenia [31][32][33][34] .…”

Section: Discussionsupporting

confidence: 84%

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Internal representation of future interactions in rats

Dvorakova,

Lobellova,

Manubens-Coda

et al. 2023

Preprint

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SUMMARYAnimals and humans receive the most critical information from parts of the environment that are immediately inaccessible, possibly only visually explored, and highly dynamic. The brain must effectively process potential interactions between elements in such an environment to make appropriate decisions in critical situations. We trained male Long-Evans rats to discriminate static and dynamic visuospatial stimuli displayed on an inaccessible computer screen. We demonstrated that the rats learned to discriminate dynamic visuospatial stimuli faster, with greater accuracy and shorter reaction time than complementary static stimuli. Furthermore, we provide behavioral evidence indicating that rats internally represent dynamic environments as static maps that capture meaningful future interactions. These observations highlight the ecological importance of dynamic stimuli in the outside world and support previous findings in humans that internal static representations can encapsulate relevant spatiotemporal information of dynamic environments. Such a mechanism would allow animals and humans to process complex time-changing situations neatly.

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Section: Discussionsupporting

confidence: 84%

“…The CIR functions as a static map, spatially encoding all possible interactions between individual elements in a given environment, effectively removing time from the dynamic situation while retaining all the necessary information for navigation 20 . The existence of time compaction in humans has been recently demonstrated 21 .…”

Section: Introductionmentioning

confidence: 99%

Internal representation of future interactions in rats

Dvorakova,

Lobellova,

Manubens-Coda

et al. 2023

Preprint

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“…Such an internal representation emerges as a reflective (thinking) processing of external information. Recent advances provided evidence of time compaction performed by the human brain when dealing with dynamic situations ( Villacorta-Atienza et al, 2021 ). Theoretically, such compaction occurs through an active wave propagating in a neuronal lattice ( Villacorta-Atienza et al, 2010 , 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Makarov

Lobov

Shchanikov

et al. 2022

Front. Comput. Neurosci.

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The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.

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“…1. Such a representation largely mimics the way humans perceive the real world [26]. In addition, DROID-SLAM introduces massive supervision for optical flow, depth, and pose estimation during training, which severely restricts the possibility of finetuning in new scenes if it performs poorly in new scenes.…”

Section: Introductionmentioning

confidence: 99%

DeFlowSLAM: Self-Supervised Scene Motion Decomposition for Dynamic Dense SLAM

Ye¹,

Yu²,

Lan³

et al. 2022

Preprint

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We present a novel dual-flow representation of scene motion that decomposes the optical flow into a static flow field caused by the camera motion and another dynamic flow field caused by the objects' movements in the scene. Based on this representation, we present a dynamic SLAM, dubbed DeFlowSLAM, that exploits both static and dynamic pixels in the images to solve the camera poses, rather than simply using static background pixels as other dynamic SLAM systems do. We propose a dynamic update module to train our DeFlowSLAM in a self-supervised manner, where a dense bundle adjustment layer takes in estimated static flow fields and the weights controlled by the dynamic mask and outputs the residual of the optimized static flow fields, camera poses, and inverse depths. The static and dynamic flow fields are estimated by warping the current image to the neighboring images, and the optical flow can be obtained by summing the two fields. Extensive experiments demonstrate that DeFlowSLAM generalizes well to both static and dynamic scenes as it exhibits comparable performance to the state-ofthe-art DROID-SLAM in static and less dynamic scenes while significantly outperforming DROID-SLAM in highly dynamic environments. Code and data are available on the project webpage: https://zju3dv.github.io/deflowslam/.

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Static internal representation of dynamic situations reveals time compaction in human cognition

Abstract: Graphical abstract

Cited by 10 publications

References 71 publications

Internal representation of future interactions in rats

Internal representation of future interactions in rats

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

DeFlowSLAM: Self-Supervised Scene Motion Decomposition for Dynamic Dense SLAM

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