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Volume: 1 Issue: 2 Date: August 2009
(Previous issue: Vol. 1, No. 1, May 2009)
Abstract: In the last few years, many studies in the cognitive and system neuroscience found that a consistent network of brain regions, referred to as the default network, showed high levels of activity when no explicit task was performed. Some scientists believed that the resting state activity might reflect some neural functions that consolidate the past, stabilize brain ensembles, and prepare us for the future. Here, we modeled the default network as undirected weighted graph, and then used graph theory to investigate the topological properties of the default network of the two groups of people with different intelligence levels. We found that, in both groups, the posterior cingulate cortex showed the greatest degree in comparison to the other brain regions in the default network, and that the medial temporal lobes and cerebellar tonsils were topologically separations from the other brain regions in the default network. More importantly, we found that the strength of some functional connectivities and the global efficiency of the default network were significantly different between the superior intelligence group and the average intelligence group, which indicates that the functional integration of the default network might be related to the individual intelligent performance.
Full Text from IEEE: PDF (466 KB) ; Contact the author by email
Abstract: We compared computational models and human performance on learning to solve a high-level, planning-intensive problem. Humans and models were subjected to three learning regimes: reinforcement, imitation, and instruction. We modeled learning by reinforcement (rewards) using SARSA, a softmax selection criterion and a neural network function approximator; learning by imitation using supervised learning in a neural network; and learning by instructions using a knowledge-based neural network. We had previously found that human participants who were told if their answers were correct or not (a reinforcement group) were less accurate than participants who watched demonstrations of successful solutions of the task (an imitation group) and participants who read instructions explaining how to solve the task. Furthermore, we had found that humans who learn by imitation and instructions performed more complex solution steps than those trained by reinforcement. Our models reproduced this pattern of results.
Full Text from IEEE: PDF (1469 KB) ; Contact the author by email
Abstract: This paper formulates five basic principles of developmental robotics. These principles are formulated based on some of the recurring themes in the developmental learning literature and in the author's own research. The five principles follow logically from the verification principle (postulated by Richard Sutton) which is assumed to be self-evident. This paper also gives an example of how these principles can be applied to the problem of autonomous tool use in robots.
Full Text from IEEE: PDF (198 KB); Contact the author by email
Abstract: Both body and visuo-spatial representations are supposed to be gradually acquired during the developmental process as described in cognitive and brain sciences. A typical example is face representation in a neuron (found in the ventral intraparietal (VIP) area) of which the function is not only to code for the location of visual stimuli in the head-centered reference frame, but also to connect visual sensation with tactile sensation. This paper presents a model that enables a robot to acquire such representation. The proprioception of arm posture is utilized as reference data through the ldquohand regard behavior,rdquo that is, the robot moves its hand in front of its face, and the self-organizing map (SOM) and Hebbian learning methods are applied. The simulation results are shown and discussions on the limitation of the current model and future issues are given.
Full Text from IEEE: PDF (1419 KB); Contact the author by email
Abstract: Development imposes great challenges. Internal “cortical”
representations must be autonomously generated from interactive experiences. The
eventual quality of these developed representations is of course important.
Additionally, learning must be as fast as possible—to quickly derive better
representation from limited experiences. Those who achieve both of these will
have competitive advantages. We present a cortex-inspired theory called lobe
component analysis (LCA) guided by the aforementioned dual criteria. A lobe
component represents a high concentration of probability density of the neuronal
input space. We explain how lobe components can achieve a dual—spatiotemporal
(“best” and “fastest”)—optimality, through mathematical analysis, in which we
describe how lobe components' plasticity can be temporally scheduled to take
into account the
Full Text from IEEE: PDF (1066 KB) ; Contact the author by email