Visualized Cognitive Knowledge Map Integration for P2P Networks

In the knowledge management field, knowledge map created under the client-server architecture has been widely used to direct the knowledge sharing process. Peer-to-peer (P2P) architecture has been practicable for file sharing, distributed computing, instant messaging, etc. by virtue of the increases of Internet bandwidth and personal computer capability. P2P architecture attracts researchers and practitioners to study knowledge sharing issues in its autonomy and self-organization characters. It calls for academic efforts to design knowledge management supporting system as that for client-server architecture may not be applicable. This study proposes a visualized cognitive knowledge map integration system to facilitate knowledge management on P2P networks. By using the SOM (Self-Organized Map)-like model, called Egocentric SOM (ESOM), the prototyping system can merge the external knowledge under a focal peer’s knowledge structure and present the cognitive knowledge map visually. In evaluating the proposed integration method, this study allocates abstracts of industrial research reports from an industrial technology research institute according to their corresponding author peers and generates individual cognitive knowledge maps. The results from the evaluation experiments reveal that ESOM is capable to retain individual peer’s knowledge structure while articulating with that of other peers in the cognitive knowledge network

Robust spatial memory maps encoded in networks with transient connections

The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space---a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map remains transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? Here, we demonstrate, using novel Algebraic Topology techniques, that cognitive map's stability is a generic, emergent phenomenon. The model allows evaluating the effect produced by specific physiological parameters, e.g., the distribution of connections' decay times, on the properties of the cognitive map as a whole. It also points out that spatial memory deterioration caused by weakening or excessive loss of the synaptic connections may be compensated by simulating the neuronal activity. Lastly, the model explicates functional importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity, by establishing three complementary timescales at which spatial information unfolds. Thus, the model provides a principal insight into how can the brain develop a reliable representation of the world, learn and retain memories despite complex plasticity of the underlying networks and allows studying how instabilities and memory deterioration mechanisms may affect learning process.Comment: 24 pages, 10 figures, 4 supplementary figure

The ingredients of an exosomatic cognitive map: isovists, agents and axial lines?

There is some evidence that an axial map, as used in space syntax, may be related to an underlying cognitive map in humans. However, the axial map is derived strictly from the mathematical configuration of space rather than any property of people. Hence there is a question of how a person might have embedded such a map. In this paper we report the results of several experiments which aim to improve the correlation between agent and pedestrian movement.We use a database of external occlusion points derived from isovists constructed throughout the system to provide a lookup table for agents to guide their movement. Since the table is external to the agents, we refer to the visual architecture as exosomatic. The results do improve on previous studies, but are still far from a good simulation of pedestrian movement. However, there is a philosophically important outcome from the experiments. When the agents are tuned to best performance, their movement patterns correspond to the axial structure of the system. This can be shown to be a mathematical result of their movement strategy; that is, the manifestation of movement, or the `memory' of an agent experiment, relates to the combination of the internal structure of the agent and its engagement with the environment in the form of an axial map. There are two unresolved steps from the relationship between individual and environment to human cognition: one, it cannot be shown that people do actually use occlusion points for movement, and two, even if they were to, it cannot be shown that they would use the resultant axial structure for higher level navigation decisions. Nevertheless, our results do provide evidence for a link between the individual and the axial map through embodiment of an agent-environment system, and our theory provides a mechanism for a link between the embodied map and preconditions for cognitive structure, which may in turn provide a basis for the future research into the means by which space syntax may be related to spatial cognition

Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them

Strategic paths and memory map: Exploring a building and memorizing knowledge

Restoring archaeology and architecture, we propose a 3D navigation mode based on topographic and cognitive paths. During the exploration of a 3D model, the learner can create his own memory map facilitating the appropriation and memorization of knowledge. In this article, we will correlate the exploration and creation activities. The Great Pyramid of Giza is a support to this work

Towards Teaching a Robot to Count Objects

We present here an example of incremental learning between two computational models dealing with different modalities: a model allowing to switch spatial visual attention and a model allowing to learn the ordinal sequence of phonetical numbers. Their merging via a common reward signal allows anyway to produce a cardinal counting behaviour that can be implemented on a robot

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task. First, we transform EEG activities into a sequence of topology-preserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information. Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension. Empirical evaluation on the cognitive load classification task demonstrated significant improvements in classification accuracy over current state-of-the-art approaches in this field.Comment: To be published as a conference paper at ICLR 201