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Exploratory activities seem to be crucial for our cognitive development. According to psychologists, exploration is an intrinsically rewarding behaviour. The developmental robotics aims to design computational systems that are endowed with such an intrinsic motivation mechanism. There are possible links between developmental robotics and machine learning. Affective computing takes into account emotions...
Human cognition is characterized by three important features: productivity, dynamics and grounding. These features can be integrated in a neural architecture. The representations in this architecture are not symbol tokens, that can be copied and transported. Instead, the representations always remain ldquoin siturdquo, because they are grounded in perception, action, emotion, associations and (semantic)...
The paper puts forward a concept of cognitive sensor networks and investigates a feasibility of artificial neural networks application for its realization. It describes a design of novel hierarchical configurations imitating the structural topology of brain-like architectures. They are composed from artificial neural networks distributed over network platforms with limited resources. The paper examines...
Movement seems to be the key ingredient to understanding how children perceive (and hence name) objects in their environment. This insight, which is based on Spelkepsilas experiments on language acquisition by children, is akin to Aristotlepsilas conception of object as a ldquocontinuous thingrdquo that has one and only one movement. Here we test this idea with a computer experiment in which the perceptual...
Based on indications from the neuroscience and psychology, both perception and action can be internally simulated by activating sensor and motor areas in the brain without external sensory input or without any resulting overt behavior. This hypothesis, however, can be highly useful in the real robot applications. The robot, for instance, can cover some of the corrupted sensory inputs by replacing...
Listening to music, as per clinical neuro science, involves many cognitive components with distinct brain substrates and its study has advanced greatly in the last three decades. But the studies of Indian music and its influence in the brain have not yet been studied. This article presents sequence of image processing steps using statistical parametric mapping for the analysis of fMRI brain structures...
Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is strongly associated with the activity of the prefrontal cortex. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing...
Assessment of sensory, motor and cognitive function of stroke subjects provide important information to guide patient rehabilitation. As many of the currently used measures are inherently subjective and use course rating scales, here we propose a hierarchical ensemble network that can automatically identify stroke patients and assess their upper limb functionality objectively, based on experimental...
Machine learning has made great progress during the last decades and is being deployed in a wide range of applications. However, current machine learning techniques are far from sufficient for achieving human-level intelligence. Here we identify the properties of learners required for human-level intelligence and suggest a new direction of machine learning research, i.e. the cognitive learning approach,...
Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability...
Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The neural modeling fields theory (NMF) addresses these challenges in a non-traditional way. The main idea behind success of NMF is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more...
In neural network modeling, the goal often is to get a most specific crisp output (e.g., binary classification of objects) from neuron inputs that have multiple possible values. In this paper, we change the viewpoint and assume that the neuron is an operator that transforms binary classical logic input to the many valued logic output, e.g., changes crisp sets into fuzzy sets. In this interpretation,...
Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural models known as fusion architecture for learning, cognition and navigation (FALCON) can incorporate a priori knowledge and perform knowledge refinement and expansion through reinforcement learning. Symbolic rules are formulated...
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