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Profile Documents Logout
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Slide 1
Slide 1

Lecture Slides
Lecture Slides

...  The term AI was first time used in 1956 by John McCarthy. The term Computational Intelligence (CI) was first time used in 1994 to mainly cover areas such as neural networks, evolutionary algorithms and fuzzy logic.  In this lecture we will focus only on neural network based algorithms because of ...
a Temporal-Causal Network Modelling Approach
a Temporal-Causal Network Modelling Approach

... form of temporal-causal networks, which can be automatically transformed into executable numerical model representations. Dedicated software is available to support designing models in a conceptual or graphical manner, and automatically transforming them into an executable format and performing simu ...
lgn - cinpla
lgn - cinpla

... feedback from the visual cortex. One would therefore expect the LGN to be more heavily influenced by visual cortex and the response not so similar to the input from retina. The role of this massive feedback has not been clearly identified, and the functional role of the LGN is therefore poorly under ...
The language of the brain
The language of the brain

... for increasing the strengths of synapses—an important process in forming long-term memories. A synapse is said to be strengthened when the firing of a neuron on one side of a synapse leads the neuron on the other side of the synapse to register a stronger response. In 1997 Henry Markram and Bert Sak ...
Self-Organizing Map Considering False Neighboring Neuron
Self-Organizing Map Considering False Neighboring Neuron

System Intelligence, Knowledge Systems and Darwin
System Intelligence, Knowledge Systems and Darwin

... My aim is to dig deeper into some essentials of Systems Intelligence (Saarinen et al. 2004) by using tools of System Analysis and applying an evolutionary model of knowledge generation. I hope to find explanations for some Systems Intelligence fundaments and answers to the question: Why is Systems I ...
the file
the file

...  Artificial neural networks are the combination of artificial neurons  After testing and analysing various neural networks we found that the CCNN is the best for the application domain under consideration.  The CCNN is a new architecture and is a generative, feed forward, supervised learning algo ...
Artificial Intelligent Application to Power System Protection
Artificial Intelligent Application to Power System Protection

... operate slowly. And vice versa, the faster is the relay, the more it tends to operate falsely. The problems listed below reflect the current practice in power system protection. There are basically two ways to mitigate the problem of limited recognition power of the classical relaying principles. On ...
Brain-Like Learning Directly from Dynamic Cluttered Natural Video
Brain-Like Learning Directly from Dynamic Cluttered Natural Video

Maximum Likelihood
Maximum Likelihood

... of freedom equal to (pf − pr ). This χ2 is often called a likelihood ratio χ2 or LR χ2 . The p value of the χ2 is the probability of randomly selecting a χ2 from a “hat” of χ2 s with (pf − pr ) degrees of freedom that is greater than the χ2 observed in the data analysis, i.e., p(χ2 > χ2obs ). If the ...
ANN Training
ANN Training

... Z (%) – Necking ...
Neural Networks
Neural Networks

Print this Page Presentation Abstract Program#/Poster#: 532.07/GG10
Print this Page Presentation Abstract Program#/Poster#: 532.07/GG10

... *M. JADI, T. J. SEJNOWSKI; Salk Inst., La Jolla, CA ...
Evolving Connectionist and Fuzzy-Connectionist Systems for
Evolving Connectionist and Fuzzy-Connectionist Systems for

LeCun - NYU Computer Science
LeCun - NYU Computer Science

Template for designing a research poster
Template for designing a research poster

... • Areas of growth: o Discovering more material systems displaying memristive behavior, o Shifting the focus from one of characterization to one of implementation. o Researching the best way to integrate memristor arrays with CMOS circuits One thing seems clear: the road to truly powerful neuromorphi ...
neuron models and basic learning rules
neuron models and basic learning rules

... • In general, there are many different kinds of activation functions. • The step function used in the McCulloch-Pitts model is simply one of them. • Because the activation function takes only two values, this model is called discrete neuron. • To make the neuron learnable, some kind of continuous fu ...
What does the eye tell the brain? Development of a system for the large-scale recording of retinal output activity
What does the eye tell the brain? Development of a system for the large-scale recording of retinal output activity

Neural Nets: introduction
Neural Nets: introduction

... stuff that dies when you poke it around • To understand a new style of computation – Inspired by neurons and their adaptive connections – Very different style from sequential computation • should be good for things that brains are good at (e.g. vision) • Should be bad for things that brains are bad ...
Automated Endoscope Navigation and Advisory System from
Automated Endoscope Navigation and Advisory System from

... of not needing to consider all the possible trees that could be constructed from purely objective data. However, the possible ignorance of some interacting variables will generate many probabilistic networks that could closely approximate the given observed data. ...
PPT - Sheffield Department of Computer Science
PPT - Sheffield Department of Computer Science

Simulating Mirror Neurons
Simulating Mirror Neurons

YAPAY SİNİR AĞLARINA GİRİŞ
YAPAY SİNİR AĞLARINA GİRİŞ

Lecture 22 clustering
Lecture 22 clustering

... Introduction to SOM • Both SOM and LVQ are proposed by T. Kohonen. • Biological motivations: Different regions of a brain (cerebral cortex) seem to tune into different tasks. Particular location of the neural response of the "map" often directly corresponds to specific modality and quality of sensor ...
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Neural modeling fields

Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
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