1,095 research outputs found
On microelectronic self-learning cognitive chip systems
After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory.
From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research.
And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting
conscious phenomena should crucially be restricted to extremely well defined constraints.
Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details.
In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche
Cerebellar models of associative memory: Three papers from IEEE COMPCON spring 1989
Three papers are presented on the following topics: (1) a cerebellar-model associative memory as a generalized random-access memory; (2) theories of the cerebellum - two early models of associative memory; and (3) intelligent network management and functional cerebellum synthesis
A DenseNet-based method for decoding auditory spatial attention with EEG
Auditory spatial attention detection (ASAD) aims to decode the attended
spatial location with EEG in a multiple-speaker setting. ASAD methods are
inspired by the brain lateralization of cortical neural responses during the
processing of auditory spatial attention, and show promising performance for
the task of auditory attention decoding (AAD) with neural recordings. In the
previous ASAD methods, the spatial distribution of EEG electrodes is not fully
exploited, which may limit the performance of these methods. In the present
work, by transforming the original EEG channels into a two-dimensional (2D)
spatial topological map, the EEG data is transformed into a three-dimensional
(3D) arrangement containing spatial-temporal information. And then a 3D deep
convolutional neural network (DenseNet-3D) is used to extract temporal and
spatial features of the neural representation for the attended locations. The
results show that the proposed method achieves higher decoding accuracy than
the state-of-the-art (SOTA) method (94.4% compared to XANet's 90.6%) with
1-second decision window for the widely used KULeuven (KUL) dataset, and the
code to implement our work is available on Github:
https://github.com/xuxiran/ASAD_DenseNe
Human Factors and Neurophysiological Metrics in Air Traffic Control: a Critical Review
International audienceThis article provides the reader a focused and organised review of the research progresses on neurophysiological indicators, also called “neurometrics”, to show how neurometrics could effectively address some of the most important Human Factors (HFs) needs in the Air Traffic Management (ATM) field. The state of the art on the most involved HFs and related cognitive processes (e.g. mental workload, cognitive training) is presented together with examples of possible applications in the current and future ATM scenarios, in order to better understand and highlight the available opportunities of such neuroscientific applications. Furthermore, the paper will discuss the potential enhancement that further research and development activities could bring to the efficiency and safety of the ATM service
Dynamic coordination in brain and mind
Our goal here is to clarify the concept of 'dynamic coordination', and to note major issues that it raises for the cognitive neurosciences. In general, coordinating interactions are those that produce coherent and relevant overall patterns of activity, while preserving the essential individual identities and functions of the activities coordinated. 'Dynamic coordination' is the coordination that is created on a moment-by-moment basis so as to deal effectively with unpredictable aspects of the current situation. We distinguish different computational goals for dynamic coordination, and outline issues that arise concerning local cortical circuits, brain systems, cognition, and evolution. Our focus here is on dynamic coordination by widely distributed processes of self-organisation, but we also discuss the role of central executive processes
Dynamic coordination in brain and mind
Our goal here is to clarify the concept of 'dynamic coordination', and to note major issues that it raises for the cognitive neurosciences. In general, coordinating interactions are those that produce coherent and relevant overall patterns of activity, while preserving the essential individual identities and functions of the activities coordinated. 'Dynamic coordination' is the coordination that is created on a moment-by-moment basis so as to deal effectively with unpredictable aspects of the current situation. We distinguish different computational goals for dynamic coordination, and outline issues that arise concerning local cortical circuits, brain systems, cognition, and evolution. Our focus here is on dynamic coordination by widely distributed processes of self-organisation, but we also discuss the role of central executive processes
Rapid, parallel path planning by propagating wavefronts of spiking neural activity
Efficient path planning and navigation is critical for animals, robotics,
logistics and transportation. We study a model in which spatial navigation
problems can rapidly be solved in the brain by parallel mental exploration of
alternative routes using propagating waves of neural activity. A wave of
spiking activity propagates through a hippocampus-like network, altering the
synaptic connectivity. The resulting vector field of synaptic change then
guides a simulated animal to the appropriate selected target locations. We
demonstrate that the navigation problem can be solved using realistic, local
synaptic plasticity rules during a single passage of a wavefront. Our model can
find optimal solutions for competing possible targets or learn and navigate in
multiple environments. The model provides a hypothesis on the possible
computational mechanisms for optimal path planning in the brain, at the same
time it is useful for neuromorphic implementations, where the parallelism of
information processing proposed here can fully be harnessed in hardware
Towards a vygotskyan cognitive robotics: the role of language as a cognitive tool
Cognitive Robotics can be defined as the study of cognitive phenomena by their modeling in physical artifacts such as robots. This is a very lively and fascinating field which has already given fundamental contributions to our understanding of natural cognition. Nonetheless, robotics has to date addressed mainly very basic, low-level cognitive phenomena like sensory-motor coordination, perception, and navigation, and it is not clear how the current approach might scale up to explain high-level human cognition. In this paper we argue that a promising way to do that is to merge current ideas and methods of \u27embodied cognition\u27 with the Russian tradition of theoretical psychology which views language not only as a communication system but also as a cognitive tool, that is by developing a Vygotskyan Cognitive Robotics. We substantiate this idea by discussing several domains in which language can improve basic cognitive abilities and permit the development of high-level cognition: learning, categorization, abstraction, memory, voluntary control, and mental life
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