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Valeri A. Makarov - One of the best experts on this subject based on the ideXlab platform.
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Static Internal Representation of dynamic situations reveals time compaction in human cognition
Journal of advanced research, 2020Co-Authors: José Antonio Villacorta-atienza, Carlos Tapia, Sergio Diez-hermano, Abel Sanchez-jimenez, Sergey Lobov, Nadia Krilova, Antonio Murciano, Gabriela E. López-tolsa, Ricardo Pellón, Valeri A. MakarovAbstract:Abstract Introduction The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on Internal Representations of the environment. Whereas neural mechanisms involved in the Internal Representation of space are becoming known, entire spatiotemporal cognition remains a challenge. Growing experimental evidence suggests that brain mechanisms devoted to spatial cognition may also participate in spatiotemporal information processing. Objectives The time compaction hypothesis postulates that the brain represents both static and dynamic situations as purely static maps. Such an Internal reduction of the external complexity allows humans to process time-changing situations in real-time efficiently. According to time compaction, there may be a deep inner similarity between the Representation of conventional static and dynamic visual stimuli. Here, we test the hypothesis and report the first experimental evidence of time compaction in humans. Methods We engaged human subjects in a discrimination-learning task consisting in the classification of static and dynamic visual stimuli. When there was a hidden correspondence between static and dynamic stimuli due to time compaction, the learning performance was expected to be modulated. We studied such a modulation experimentally and by a computational model. Results The collected data validated the predicted learning modulation and confirmed that time compaction is a salient cognitive strategy adopted by the human brain to process time-changing situations. Mathematical modelling supported the finding. We also revealed that men are more prone to exploit time compaction in accordance with the context of the hypothesis as a cognitive basis for survival. Conclusions The static Internal Representation of dynamic situations is a human cognitive mechanism involved in decision-making and strategy planning to cope with time-changing environments. The finding opens a new venue to understand how humans efficiently interact with our dynamic world and thrive in nature.
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Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II - Compact Internal Representation of Dynamic Environments: Simple Memory Structures for Complex Situations
Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II, 2013Co-Authors: José Antonio Villacorta-atienza, Manuel G. Velarde, Valeri A. MakarovAbstract:In this chapter the novel concept of Compact Internal Representation (CIR) is introduced as a generalization of the Internal Representation extensively used in literature as a base for cognition and consciousness. CIR is suitable to represent dynamic environments and their potential interactions with the agent as static (time-independent) structures, suitable to be stored, managed, compared and recovered by memory. In this work the application of CIR as the cognitive core of moving autonomous artificial agents is presented in the context of collision avoidance against dynamical obstacles. The structure that emerges, even if not directly related to the insect neurobiology, is quite simple and could enhance the capabilities of the computational model already presented in the previous chapter, in view of its robotic implementation.
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Compact Internal Representation as a protocognitive scheme for robots in dynamic environments
Bioelectronics Biomedical and Bioinspired Systems V; and Nanotechnology V, 2011Co-Authors: José Antonio Villacorta-atienza, Luis Salas, Luis Alba, Manuel G. Velarde, Valeri A. MakarovAbstract:Animals for surviving have developed cognitive abilities allowing them an abstract Representation of the environment. This Internal Representation (IR) could contain a huge amount of information concerning the evolution and interactions of the elements in their surroundings. The complexity of this information should be enough to ensure the maximum fidelity in the Representation of those aspects of the environment critical for the agent, but not so high to prevent the management of the IR in terms of neural processes, i.e. storing, retrieving, etc. One of the most subtle points is the inclusion of temporal information, necessary in IRs of dynamic environments. This temporal information basically introduces the environmental information for each moment, so the information required to generate the IR would eventually be increased dramatically. The inclusion of this temporal information in biological neural processes remains an open question. In this work we propose a new IR, the Compact Internal Representation (CIR), based on the compaction of spatiotemporal information into only space, leading to a stable structure (with no temporal dimension) suitable to be the base for complex cognitive processes, as memory or learning. The Compact Internal Representation is especially appropriate for be implemented in autonomous robots because it provides global strategies for the interaction with real environments (roving robots, manipulators, etc.). This paper presents the mathematical basis of CIR hardware implementation in the context of navigation in dynamic environments. The aim of such implementation is the obtaining of free-collision trajectories under the requirements of an optimal performance by means of a fast and accurate process.
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fpga implementation of a modified fitzhugh nagumo neuron based causal neural network for compact Internal Representation of dynamic environments
Bioelectronics Biomedical and Bioinspired Systems V; and Nanotechnology V, 2011Co-Authors: L Salasparacuellos, Luis Alba, Jose Antonio Villacortaatienza, Valeri A. MakarovAbstract:Animals for surviving have developed cognitive abilities allowing them an abstract Representation of the environment. This Internal Representation (IR) may contain a huge amount of information concerning the evolution and interactions of the animal and its surroundings. The temporal information is needed for IRs of dynamic environments and is one of the most subtle points in its implementation as the information needed to generate the IR may eventually increase dramatically. Some recent studies have proposed the compaction of the spatiotemporal information into only space, leading to a stable structure suitable to be the base for complex cognitive processes in what has been called Compact Internal Representation (CIR). The Compact Internal Representation is especially suited to be implemented in autonomous robots as it provides global strategies for the interaction with real environments. This paper describes an FPGA implementation of a Causal Neural Network based on a modified FitzHugh-Nagumo neuron to generate a Compact Internal Representation of dynamic environments for roving robots, developed under the framework of SPARK and SPARK II European project, to avoid dynamic and static obstacles.
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Compact Internal Representation of dynamic situations: neural network implementing the causality principle
Biological cybernetics, 2010Co-Authors: José Antonio Villacorta-atienza, Manuel G. Velarde, Valeri A. MakarovAbstract:Animals for survival in complex, time-evolving environments can estimate in a "single parallel run" the fitness of different alternatives. Understanding of how the brain makes an effective compact Internal Representation (CIR) of such dynamic situations is a challenging problem. We propose an artificial neural network capable of creating CIRs of dynamic situations describing the behavior of a mobile agent in an environment with moving obstacles. The network exploits in a mental world model the principle of causality, which enables reduction of the time-dependent structure of real situations to compact static patterns. It is achieved through two concurrent processes. First, a wavefront representing the agent's virtual present interacts with mobile and immobile obstacles forming static effective obstacles in the network space. The dynamics of the corresponding neurons in the virtual past is frozen. Then the diffusion-like process relaxes the remaining neurons to a stable steady state, i.e., a CIR is given by a single point in the multidimensional phase space. Such CIRs can be unfolded into real space for execution of motor actions, which allows a flexible task-dependent path planning in realistic time-evolving environments. Besides, the proposed network can also work as a part of "autonomous thinking", i.e., some mental situations can be supplied for evaluation without direct motor execution. Finally we hypothesize the existence of a specific neuronal population responsible for detection of possible time-space coincidences of the animal and moving obstacles.
José Antonio Villacorta-atienza - One of the best experts on this subject based on the ideXlab platform.
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Static Internal Representation of dynamic situations reveals time compaction in human cognition
Journal of advanced research, 2020Co-Authors: José Antonio Villacorta-atienza, Carlos Tapia, Sergio Diez-hermano, Abel Sanchez-jimenez, Sergey Lobov, Nadia Krilova, Antonio Murciano, Gabriela E. López-tolsa, Ricardo Pellón, Valeri A. MakarovAbstract:Abstract Introduction The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on Internal Representations of the environment. Whereas neural mechanisms involved in the Internal Representation of space are becoming known, entire spatiotemporal cognition remains a challenge. Growing experimental evidence suggests that brain mechanisms devoted to spatial cognition may also participate in spatiotemporal information processing. Objectives The time compaction hypothesis postulates that the brain represents both static and dynamic situations as purely static maps. Such an Internal reduction of the external complexity allows humans to process time-changing situations in real-time efficiently. According to time compaction, there may be a deep inner similarity between the Representation of conventional static and dynamic visual stimuli. Here, we test the hypothesis and report the first experimental evidence of time compaction in humans. Methods We engaged human subjects in a discrimination-learning task consisting in the classification of static and dynamic visual stimuli. When there was a hidden correspondence between static and dynamic stimuli due to time compaction, the learning performance was expected to be modulated. We studied such a modulation experimentally and by a computational model. Results The collected data validated the predicted learning modulation and confirmed that time compaction is a salient cognitive strategy adopted by the human brain to process time-changing situations. Mathematical modelling supported the finding. We also revealed that men are more prone to exploit time compaction in accordance with the context of the hypothesis as a cognitive basis for survival. Conclusions The static Internal Representation of dynamic situations is a human cognitive mechanism involved in decision-making and strategy planning to cope with time-changing environments. The finding opens a new venue to understand how humans efficiently interact with our dynamic world and thrive in nature.
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Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II - Compact Internal Representation of Dynamic Environments: Simple Memory Structures for Complex Situations
Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II, 2013Co-Authors: José Antonio Villacorta-atienza, Manuel G. Velarde, Valeri A. MakarovAbstract:In this chapter the novel concept of Compact Internal Representation (CIR) is introduced as a generalization of the Internal Representation extensively used in literature as a base for cognition and consciousness. CIR is suitable to represent dynamic environments and their potential interactions with the agent as static (time-independent) structures, suitable to be stored, managed, compared and recovered by memory. In this work the application of CIR as the cognitive core of moving autonomous artificial agents is presented in the context of collision avoidance against dynamical obstacles. The structure that emerges, even if not directly related to the insect neurobiology, is quite simple and could enhance the capabilities of the computational model already presented in the previous chapter, in view of its robotic implementation.
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Compact Internal Representation as a protocognitive scheme for robots in dynamic environments
Bioelectronics Biomedical and Bioinspired Systems V; and Nanotechnology V, 2011Co-Authors: José Antonio Villacorta-atienza, Luis Salas, Luis Alba, Manuel G. Velarde, Valeri A. MakarovAbstract:Animals for surviving have developed cognitive abilities allowing them an abstract Representation of the environment. This Internal Representation (IR) could contain a huge amount of information concerning the evolution and interactions of the elements in their surroundings. The complexity of this information should be enough to ensure the maximum fidelity in the Representation of those aspects of the environment critical for the agent, but not so high to prevent the management of the IR in terms of neural processes, i.e. storing, retrieving, etc. One of the most subtle points is the inclusion of temporal information, necessary in IRs of dynamic environments. This temporal information basically introduces the environmental information for each moment, so the information required to generate the IR would eventually be increased dramatically. The inclusion of this temporal information in biological neural processes remains an open question. In this work we propose a new IR, the Compact Internal Representation (CIR), based on the compaction of spatiotemporal information into only space, leading to a stable structure (with no temporal dimension) suitable to be the base for complex cognitive processes, as memory or learning. The Compact Internal Representation is especially appropriate for be implemented in autonomous robots because it provides global strategies for the interaction with real environments (roving robots, manipulators, etc.). This paper presents the mathematical basis of CIR hardware implementation in the context of navigation in dynamic environments. The aim of such implementation is the obtaining of free-collision trajectories under the requirements of an optimal performance by means of a fast and accurate process.
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Compact Internal Representation of dynamic situations: neural network implementing the causality principle
Biological cybernetics, 2010Co-Authors: José Antonio Villacorta-atienza, Manuel G. Velarde, Valeri A. MakarovAbstract:Animals for survival in complex, time-evolving environments can estimate in a "single parallel run" the fitness of different alternatives. Understanding of how the brain makes an effective compact Internal Representation (CIR) of such dynamic situations is a challenging problem. We propose an artificial neural network capable of creating CIRs of dynamic situations describing the behavior of a mobile agent in an environment with moving obstacles. The network exploits in a mental world model the principle of causality, which enables reduction of the time-dependent structure of real situations to compact static patterns. It is achieved through two concurrent processes. First, a wavefront representing the agent's virtual present interacts with mobile and immobile obstacles forming static effective obstacles in the network space. The dynamics of the corresponding neurons in the virtual past is frozen. Then the diffusion-like process relaxes the remaining neurons to a stable steady state, i.e., a CIR is given by a single point in the multidimensional phase space. Such CIRs can be unfolded into real space for execution of motor actions, which allows a flexible task-dependent path planning in realistic time-evolving environments. Besides, the proposed network can also work as a part of "autonomous thinking", i.e., some mental situations can be supplied for evaluation without direct motor execution. Finally we hypothesize the existence of a specific neuronal population responsible for detection of possible time-space coincidences of the animal and moving obstacles.
Pitoyo Hartono - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - Visualization of topographical Internal Representation of learning robots
2020 International Joint Conference on Neural Networks (IJCNN), 2020Co-Authors: Shiori Kuramoto, Hideyuki Sawada, Pitoyo HartonoAbstract:The objective of this study is to understand the learned-strategy of neural network-controlled robots in relation to their physical learning environments by visualizing the Internal layer of the neural network. During the past few years, neural network-controlled robots that are able to learn in physical environments are becoming more common. While they can autonomously acquire strategy without human supervisions, it is becoming difficult to understand their strategy, especially when the robots, their environments and their tasks are complicated. In the critical fields that involve human safety, as in self-driving vehicles or medical robots, it is important for human to understand the strategies of the robots. In this preliminary study, we propose a hierarchical neural network with a two-dimensional topographical Internal Representation for training robots in physical environments. The 2D Representation can then be visualized and analyzed to allow us to intuitively understand the input-output strategy of the robots in the context of their learning environments. In this paper, we explain about the learning dynamics of the neural network and the visual analysis of some physical experiments.
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classifier with hierarchical topographical maps as Internal Representation
International Conference on Intelligent Engineering Systems, 2015Co-Authors: Thomas Trappenberg, Paul Hollensen, Pitoyo HartonoAbstract:In this study we want to connect our previously proposed context-relevant topographical maps with the deep learning community. Our architecture is a classifier with hidden layers that are hierarchical two-dimensional topographical maps. These maps differ from the conventional self-organizing maps in that their organizations are influenced by the context of the data labels in a top-down manner. In this way bottom-up and top-down learning are combined in a biologically relevant Representational learning setting. Compared to our previous work, we are here specifically elaborating the model in a more challenging setting compared to our previous experiments and to advance more hidden Representation layers to bring our discussions into the context of deep Representational learning.
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SCIS&ISIS - Internal Representation of sensory information for training autonomous robot
The 6th International Conference on Soft Computing and Intelligent Systems and The 13th International Symposium on Advanced Intelligence Systems, 2012Co-Authors: Pitoyo Hartono, Thomas TrappenbergAbstract:In this paper we report on our experiments in training an autonomous robot using a hierarchical neural network containing a topographical map in its hidden layer. The map topologically organizes the sensory information of the robot and propagates this information to the next layer that is trained in supervised manner. Through some physical experiments, we show that the order in the Internal Representation is important in supporting the success of the supervised learning of the robot to acquire a good strategy for operating in physical environments.
Lijuan Duan - One of the best experts on this subject based on the ideXlab platform.
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learning Internal Representation of visual context in a neural coding network
International Conference on Artificial Neural Networks, 2010Co-Authors: Jun Miao, Baixian Zou, Laiyun Qing, Lijuan DuanAbstract:Visual context plays a significant role in humans' gaze movement for target searching. How to transform the visual context into the Internal Representation of a brain-like neural network is an interesting issue. Population cell coding is a neural Representation mechanism which was widely discovered in primates' visual neural system. This paper presents a biologically inspired neural network model which uses a population cell coding mechanism for visual context Representation and target searching. Experimental results show that the population-cell-coding generally performs better than the single-cell-coding system.
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ICANN (1) - Learning Internal Representation of visual context in a neural coding network
Artificial Neural Networks – ICANN 2010, 2010Co-Authors: Jun Miao, Baixian Zou, Laiyun Qing, Lijuan DuanAbstract:Visual context plays a significant role in humans' gaze movement for target searching. How to transform the visual context into the Internal Representation of a brain-like neural network is an interesting issue. Population cell coding is a neural Representation mechanism which was widely discovered in primates' visual neural system. This paper presents a biologically inspired neural network model which uses a population cell coding mechanism for visual context Representation and target searching. Experimental results show that the population-cell-coding generally performs better than the single-cell-coding system.
Jean-sébastien Blouin - One of the best experts on this subject based on the ideXlab platform.
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the Internal Representation of head orientation differs for conscious perception and balance control
The Journal of Physiology, 2017Co-Authors: Brian H. Dalton, Brandon G. Rasman, Timothy J Inglis, Jean-sébastien BlouinAbstract:KEY POINTS We tested perceived head-on-feet orientation and the direction of vestibular-evoked balance responses in passively- and actively-held head-turned postures The direction of vestibular-evoked balance responses was not aligned with perceived head-on-feet orientation while maintaining prolonged passively-held head-turned postures. Furthermore, static visual cues of head-on-feet orientation did not update the estimate of head posture for the balance controller A prolonged actively-held head-turned posture did not elicit a rotation in the direction of the vestibular-evoked balance response despite a significant rotation in perceived angular head posture It is proposed that conscious perception of head posture and the transformation of vestibular signals for standing balance relying on this head posture are not dependent on the same Internal Representation. Rather, the balance system may operate under its own sensorimotor principles, which are partly independent from perception Abstract Vestibular signals used for balance control must be integrated with other sensorimotor cues to allow transformation of descending signals according to an Internal Representation of body configuration. We explored two alternate models of sensorimotor integration that propose 1) a single Internal Representation of head-on-feet orientation is responsible for perceived postural orientation and standing balance or 2) conscious perception and balance control arxe driven by separate Internal Representations. During three experiments, participants stood quietly while passively or actively maintaining a prolonged head-turned posture (>10 min). Throughout the trials, participants intermittently reported their perceived head angular position and subsequently electrical vestibular stimuli to were delivered to elicit whole-body balance responses. Visual recalibration of head-on-feet posture was used to determine whether static visual cues are used to update the Internal Representation of body configuration for perceived orientation and standing balance. All three experiments involved situations in which the vestibular-evoked balance response was not orthogonal with perceived head-on-feet orientation, regardless of visual information provided. For prolonged head-turned postures, balance responses consistent with actual head-on-feet posture only occurred during the active condition. Our results indicate that conscious perception of head-on-feet posture and vestibular control of balance do not rely on the same Internal Representation, but instead treat sensorimotor cues in parallel and may arrive at different conclusions regarding head-on-feet posture. The balance system appears to bypass static visual cues of postural orientation and mainly use other sensorimotor signals of head-on-feet position to transform vestibular signals of head motion, a mechanism appropriate for most daily activities. This article is protected by copyright. All rights reserved
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The Internal Representation of head orientation differs for conscious perception and balance control
The Journal of physiology, 2017Co-Authors: Brian H. Dalton, Brandon G. Rasman, J. Timothy Inglis, Jean-sébastien BlouinAbstract:We tested perceived head-on-feet orientation and the direction of vestibular-evoked balance responses in passively and actively held head-turned postures. The direction of vestibular-evoked balance responses was not aligned with perceived head-on-feet orientation while maintaining prolonged passively held head-turned postures. Furthermore, static visual cues of head-on-feet orientation did not update the estimate of head posture for the balance controller. A prolonged actively held head-turned posture did not elicit a rotation in the direction of the vestibular-evoked balance response despite a significant rotation in perceived angular head posture. It is proposed that conscious perception of head posture and the transformation of vestibular signals for standing balance relying on this head posture are not dependent on the same Internal Representation. Rather, the balance system may operate under its own sensorimotor principles, which are partly independent from perception. Vestibular signals used for balance control must be integrated with other sensorimotor cues to allow transformation of descending signals according to an Internal Representation of body configuration. We explored two alternative models of sensorimotor integration that propose (1) a single Internal Representation of head-on-feet orientation is responsible for perceived postural orientation and standing balance or (2) conscious perception and balance control are driven by separate Internal Representations. During three experiments, participants stood quietly while passively or actively maintaining a prolonged head-turned posture (>10 min). Throughout the trials, participants intermittently reported their perceived head angular position, and subsequently electrical vestibular stimuli were delivered to elicit whole-body balance responses. Visual recalibration of head-on-feet posture was used to determine whether static visual cues are used to update the Internal Representation of body configuration for perceived orientation and standing balance. All three experiments involved situations in which the vestibular-evoked balance response was not orthogonal to perceived head-on-feet orientation, regardless of the visual information provided. For prolonged head-turned postures, balance responses consistent with actual head-on-feet posture occurred only during the active condition. Our results indicate that conscious perception of head-on-feet posture and vestibular control of balance do not rely on the same Internal Representation, but instead treat sensorimotor cues in parallel and may arrive at different conclusions regarding head-on-feet posture. The balance system appears to bypass static visual cues of postural orientation and mainly use other sensorimotor signals of head-on-feet position to transform vestibular signals of head motion, a mechanism appropriate for most daily activities. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.