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Website for Table of Contents, Abstracts & Authors' emails: http://research.microsoft.com/~zhang/IEEE-TAMD/
Volume: 3 Issue: 4 Date: December 2011
Link to IEEE: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6097099
(Previous issue:Vol. 3, No. 3, September 2011)
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Abstract:Recent experimental results have accumulated evidence that the neurons can change their response characteristics to adapt to the variations of the synaptic inputs, which is the so-called neuronal intrinsic plasticity mechanism. In this paper, we present a new model on neuronal intrinsic plasticity. We first show that the probability distribution of the neuronal firing rates is more suitable to be represented as a Weibull distribution than an exponential distribution. Then, we derive the intrinsic plasticity model based on information theory. This study provides a more realistic model for further research on the effects of intrinsic plasticity on various brain functions and dynamics.
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Abstract:Dynamic neural fields are recurrent neural networks which aim at modeling cortical activity evolution both in space and time. A self-organized formation of these fields has been rarely explored previously. The main reason for this is that learning-induced changes in effective connectivity constitute a severe problem with respect to network stability. In this paper, we present a novel network model which is able to self-organize even in face of experience-driven changes in the synaptic strengths of all connections. Key to the model is the incorporation of homeostatic mechanisms which explicitly address network stability. These mechanisms regulate activity of individual neurons in a similar manner as cortical activity is controlled. Namely, our model implements the homeostatic principles of synaptic scaling and intrinsic plasticity. By using fully plastic within-field connections our model further decouples learning from topological constraints. For this reason, we propose to incorporate an additional process which facilitates the development of topology preserving mappings. This process minimizes the wiring length between neurons. We thoroughly evaluated the model using artificial data as well as continuous speech. Our results demonstrate that the network is able to self-organize, maintains stable activity levels, and remains adaptive to variations in input strength and input distribution.
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Abstract:The paper proposes a novel research framework for building probabilistic computational neurogenetic models (pCNGM). The pCNGM is a multilevel modeling framework inspired by the multilevel information processes in the brain. The framework comprises a set of several dynamic models, namely low (molecular) level models, a more abstract dynamic model of a protein regulatory network (PRN) and a probabilistic spiking neural network model (pSNN), all linked together. Genes/proteins from the PRN control parameters of the pSNN and the spiking activity of the pSNN provides feedback to the PRN model. The overall spatio-temporal pattern of spiking activity of the pSNN is interpreted as the highest level state of the pCNGM. The paper demonstrates that this framework can be used for modeling both artificial cognitive systems and brain processes. In the former application, the pCNGM utilises parameters that correspond to sensory elements and neuromodulators. In the latter application a pCNGM uses data obtained from relevant genes/proteins to model their dynamic interaction that matches data related to brain development, higher-level brain function or disorder in different scenarios. An exemplar case study on Alzheimer's Disease is presented. Future applications of pCNGM are discussed.
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Abstract:The PURR-PUSS system (PP) is a versatile model of a human-like brain, designed to be implemented in parallel hardware and embodied in the head of a robot moving in the real world. The aim of the research with PP is to try out mechanisms for learning, intelligence and consciousness. Limitations of resources have dictated that the experiments with PP are made on a personal computer by simulating the brain and robot body in a microworld. The unique features of PP are multiple context and novelty-seeking. In this paper, a squash-pop microworld is described first, so that concrete examples can be given for a brief review of the PP system, followed by two new features called trail memory, to realize Baars' global workspace, and belief memory, to realize Rosenthal's higher order thoughts and Johnson-Laird's conscious reasoning. The extended system, PP*, is designed to give consciousness to the subconscious PP, but higher order thoughts and conscious reasoning prove to be elusive. A definition of a conscious robot provides a measure of progress.
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From Infant Brains to Robots: A Report From the IEEE International
Conference on Development and Learning (ICDL)-International Conference on
Epigenetic Robotics (EpiRob) 2011 Conference
Cangelosi, A Triesch, J.
Digital Object Identifier 10.1109/TAMD.2011.2173009
Full Text from IEEE: PDF (22KB)