MODELING THE MIRROR: GRASP LEARNING AND ACTION
... DEDICATION ........................................................................................................................... ii ACKNOWLEDGEMENTS .....................................................................................................iii ...
... DEDICATION ........................................................................................................................... ii ACKNOWLEDGEMENTS .....................................................................................................iii ...
Inconsistent Heuristics in Theory and Practice
... happens, then these nodes must be moved back to the open list, where they might be chosen for expansion again. This phenomenon is known as node re-expansion. A* with an inconsistent heuristic may perform an exponential number of node re-expansions [32]. We present insights into this phenomenon, show ...
... happens, then these nodes must be moved back to the open list, where they might be chosen for expansion again. This phenomenon is known as node re-expansion. A* with an inconsistent heuristic may perform an exponential number of node re-expansions [32]. We present insights into this phenomenon, show ...
Adenilton José da Silva
... together with learning capabilities have numerous applications in real life problems. One limitation of artificial neural networks is the lack of an efficient algorithm to determine its optimal architecture. The main objective of this work is to verify whether we can obtain some advantage with the u ...
... together with learning capabilities have numerous applications in real life problems. One limitation of artificial neural networks is the lack of an efficient algorithm to determine its optimal architecture. The main objective of this work is to verify whether we can obtain some advantage with the u ...
PART 1 - FTP Directory Listing
... This PhD was carried out as part of the CRONOS project and one of its main achievements was the development of a method for predicting and describing the conscious states of artificial systems. This could help machine consciousness to become more scientific and it could also be used to make predicti ...
... This PhD was carried out as part of the CRONOS project and one of its main achievements was the development of a method for predicting and describing the conscious states of artificial systems. This could help machine consciousness to become more scientific and it could also be used to make predicti ...
Motif distribution, dynamical properties, and computational
... We investigate these two cortical microcircuit templates with regard to structural and functional properties. In order to evaluate the computational properties of microcircuit templates we carried out computer simulations of detailed cortical microcircuit models consisting of 560 Hodgkin–Huxley type ...
... We investigate these two cortical microcircuit templates with regard to structural and functional properties. In order to evaluate the computational properties of microcircuit templates we carried out computer simulations of detailed cortical microcircuit models consisting of 560 Hodgkin–Huxley type ...
PDF (Recognizing and Discovering Activities of Daily Living in Smart
... ubiquitous applications in fields like Ambient Assisted Living. Depending on the availability of labeled data, recognition methods can be categorized as either supervised or unsupervised. Designing a comprehensive activity recognition system that works on a real-world setting is extremely challengin ...
... ubiquitous applications in fields like Ambient Assisted Living. Depending on the availability of labeled data, recognition methods can be categorized as either supervised or unsupervised. Designing a comprehensive activity recognition system that works on a real-world setting is extremely challengin ...
artificial intelligence (luger, 6th, 2008)
... are constructing a hierarchy of theories that characterize it at multiple levels of abstraction. At the lowest levels of this hierarchy, neural networks, genetic algorithms and other forms of emergent computation have enabled us to understand the processes of adaptation, perception, embodiment, and ...
... are constructing a hierarchy of theories that characterize it at multiple levels of abstraction. At the lowest levels of this hierarchy, neural networks, genetic algorithms and other forms of emergent computation have enabled us to understand the processes of adaptation, perception, embodiment, and ...
Artificial Intelligence Illuminated
... or students of other subjects that cover Artificial Intelligence. It also is intended to be an interesting and relevant introduction to the subject for other students or individuals who simply have an interest in the subject. The book assumes very little knowledge of computer science, but does assum ...
... or students of other subjects that cover Artificial Intelligence. It also is intended to be an interesting and relevant introduction to the subject for other students or individuals who simply have an interest in the subject. The book assumes very little knowledge of computer science, but does assum ...
cortical limbic system: a computational model. PhD thesis. htt
... I wish to acknowledge some of the people who have assisted me in this work and made my experience at Glasgow so enjoyable. First and foremost, I would like to thank my supervisor Bernd Porr for believing in me, as well as the guidance he has given throughout the years. His positive energy for scienc ...
... I wish to acknowledge some of the people who have assisted me in this work and made my experience at Glasgow so enjoyable. First and foremost, I would like to thank my supervisor Bernd Porr for believing in me, as well as the guidance he has given throughout the years. His positive energy for scienc ...
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... It’s Just “Emotions” Has Taken Over… ...
... It’s Just “Emotions” Has Taken Over… ...
Machine Learning for Computer Games
... • Classification can be cast as an optimization problem • Function is number of correct classifications on some test set of examples ...
... • Classification can be cast as an optimization problem • Function is number of correct classifications on some test set of examples ...
Ch 16. Artificial Intelligence
... A single data item has been observed A memory representation has been created for it Each new object has its own configuration and memorized structure Thousands objects thousands representations ...
... A single data item has been observed A memory representation has been created for it Each new object has its own configuration and memorized structure Thousands objects thousands representations ...
Combining Classifiers: from the creation of ensembles - ICMC
... For small datasets noise injection can be used to increase the number of objects. The first studies just inserted Gaussian noise into the data set in order generate artificial objects, but this approach is dangerous and can even insert outliers. One possible method is to create new patterns only alo ...
... For small datasets noise injection can be used to increase the number of objects. The first studies just inserted Gaussian noise into the data set in order generate artificial objects, but this approach is dangerous and can even insert outliers. One possible method is to create new patterns only alo ...
“left or right” Decision-making beyond
... How do we make decisions? A comprehensive answer to this multifaceted question cannot be given within the scope of one single scientific discipline. Fields from philosophy to economic sciences aim to shed light on particular aspects of decision-making, approaching the topic from different perspectiv ...
... How do we make decisions? A comprehensive answer to this multifaceted question cannot be given within the scope of one single scientific discipline. Fields from philosophy to economic sciences aim to shed light on particular aspects of decision-making, approaching the topic from different perspectiv ...
A dendritic disinhibitory circuit mechanism for pathway
... with a reduced morphology (Fig. 2a; Supplementary Fig. 1). It comprises one spiking somatic compartment and multiple dendritic compartments, which are electrically coupled to the soma but otherwise independent of each other. The somatic and dendritic compartments have no spatial extent themselves. T ...
... with a reduced morphology (Fig. 2a; Supplementary Fig. 1). It comprises one spiking somatic compartment and multiple dendritic compartments, which are electrically coupled to the soma but otherwise independent of each other. The somatic and dendritic compartments have no spatial extent themselves. T ...
Stochastic neural network dynamics: synchronisation and control
... in Chapter 7. Heterogeneous coupling strengths that evolve with time, according to the Ornstein-Uhlenbeck Process, are investigated in Chapter 8; attention is directed to the influence of coupling strength noise intensity and convergence rate parameters. In Chapter 9, cumulant equations are derived ...
... in Chapter 7. Heterogeneous coupling strengths that evolve with time, according to the Ornstein-Uhlenbeck Process, are investigated in Chapter 8; attention is directed to the influence of coupling strength noise intensity and convergence rate parameters. In Chapter 9, cumulant equations are derived ...
cellular mechanisms of classical and operant conditioning A model
... En) and the same reinforcement transmitter (dopamine, DA). In addition, at least one cellular locus of plasticity (cell B51) is modified by both forms of associative learning. However, the two forms of associative learning have opposite effects on B51. Classical conditioning decreases the excitabili ...
... En) and the same reinforcement transmitter (dopamine, DA). In addition, at least one cellular locus of plasticity (cell B51) is modified by both forms of associative learning. However, the two forms of associative learning have opposite effects on B51. Classical conditioning decreases the excitabili ...
Tuning Curve Shift by Attention Modulation in Cortical Neurons: a
... substitute [I]+ = I and this allows for precise analytical calculations (shown in the Appendix). We prove, however, that our main points regarding this model do not depend on this particular choice (see Fig. 4C). When we simulate an attentional signal with inhibitory surround effect, we use r9A = 0. ...
... substitute [I]+ = I and this allows for precise analytical calculations (shown in the Appendix). We prove, however, that our main points regarding this model do not depend on this particular choice (see Fig. 4C). When we simulate an attentional signal with inhibitory surround effect, we use r9A = 0. ...
Olfactory Learning in Drosophila: Learning from Models
... Stimulus intensity affects the LI in a behavioral experiment. For electric shock as US, larger number of electric shocks increases the behavioral performance [4]. Increasing the voltage of the electric shock also increases the LI [4, 9]. At first increasing odor intensity also increases learning per ...
... Stimulus intensity affects the LI in a behavioral experiment. For electric shock as US, larger number of electric shocks increases the behavioral performance [4]. Increasing the voltage of the electric shock also increases the LI [4, 9]. At first increasing odor intensity also increases learning per ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.