The Female Brain

neural networks and brain function

Neural Networks

Artificial neural network is an inspirational technology of biological neural network. In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses, are usually formed from axons to dendrites, though other kind of connections, also possible.

An Artificial Neural Network is also called as simulated neural network of many very simple processors ("units"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections. Artificial neural networks are made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks.

HISTORY OF NEURAL NETWORKS:-

The concept neural networks started in the late-1800s as an effort to describe how the human mind performed. These ideas started being applied to computational models with Turing's B-type machines and the perceptron. In 1940  Donald Hebb made one of the first hypotheses for a mechanism of neural plasticity  (i.e. learning), Hebbian learning. Hebbian learning is considered to be a 'typical' unsupervised learning rule. In 1943 McCulloch and Pitts proposed the McCulloch-Pitts neuron model. In 1948 Alan Tuning, first proposed the concept of Neural Networks in his paper "Intelligent Machinery".  In 1949 Hebb published his book The Organization of Behavior, in which the Hebbian  learning rule was proposed. In 1958 Rosenblatt introduced the simple single layer networks now called Perceptrons. In 1969 Minsky and Papert’s book Perceptrons demonstrated the limitation of single. In layer perceptrons, and almost the whole field went into hibernation. In 1982 Hopfield published a series of papers on Hopfield networks. In 1982 Kohonen developed the Self-Organizing Maps that now bear his name. In 1986 The Back-Propagation learning algorithm for Multi-Layer Perceptrons was rediscovered and the whole field took off again. In 1990s The sub-field of Radial Basis Function Networks was developed. In 2000s The power of Ensembles of Neural Networks and Support Vector Machines becomes apparent.

NEURAL NETWORKS:-

 Neural networks take a different approach to problem solving than that of conventional computers. Conventional computers use an algorithmic approach i.e. the computer follows a set of instructions in order to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. That restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve. But computers would be so much more useful if they could do things that we don't exactly know how to do.

Neural networks process information in a similar way the human brain does. The network is composed of a large number of highly interconnected processing elements(neurons) working in parallel to solve a specific problem. Neural networks learn by example. They cannot be programmed to perform a specific task. The examples must be selected carefully otherwise useful time is wasted or even worse the network might be functioning incorrectly. The disadvantage is that because the network finds out how to solve the problem by itself, its operation can be unpredictable.

On the other hand, conventional computers use a cognitive approach to problem solving; the way the problem is to solved must be known and stated in small unambiguous instructions. These instructions are then converted to a high level language program and then into machine code that the computer can understand. These machines are totally predictable; if anything goes wrong is due to a software or hardware fault.

Neural networks and conventional algorithmic computers are not in competition but complement each other. There are tasks are more suited to an algorithmic approach like arithmetic operations and tasks that are more suited to neural networks. Even more, a large number of tasks, require systems that use a combination of the two approaches (normally a conventional computer is used to supervise the neural network) in order to perform at maximum efficiency.

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.
Other advantages include:

1. Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
2. Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time.
3. Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
4. Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

A SIMPLE NEURON:-

 An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns. In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output. If the input pattern does not belong in the taught list of input patterns, the firing rule is used to determine whether to fire or not.

 STRUCTURE:-

Artificial neural networks are quite different from the brain in terms of structure. Like the brain, however, a neural net is a massively parallel collection of small and simple processing units where the interconnections form a large part of the network's intelligence; however, in terms of scale, a brain is massively larger than a neural network, and the units used in a neural network are typically far simpler than neurons. Nevertheless, certain functions that seem exclusive to the brain such as learning, have been replicated on a simpler scale, with neural networks.

MODELS:-

A typical feed-forward neural network is a set of nodes. In computing, feed-forward normally refers to a multi-layer perceptron network in which the outputs from all neurons go to following but not preceding layers, so there are no feedback loops. Some of these are designated input nodes, some output nodes, and in-between are hidden nodes. Each connection between neurons has a numerical weight. When the network is in operation, a value will be applied to each input node -- the values being fed in by a human operator, from environmental sensors, or from some external program. Each node then passes its given value to the connections leading out from it, and on each connection the value is multiplied by the weight associated with that connection. Each node in the next layer then receives a value which is the sum of the values produced by the connections leading into it, and in each node a simple computation is performed on the value -- a sigmoid function is typical. This process is then repeated, with the results being passed through subsequent layers of nodes until the output nodes are reached. Early models (circa 1970) had a fixed number of layers. More recently, genetic algorithms are used to evolve the neural structure

CALCULATIONS:-

The sigmoid curve is often used as a transfer function because it introduces non-linearity into the network's calculations by "squashing" the neuron's activation level into the range [0,1]. The sigmoid function has the additional benefit of having an extremely simple derivative function, as required for back-propagating errors through a feed-forward neural network. Other functions with similar features can be used, most commonly tanh which squashes activations into the range of [-1,1] instead, or occasionally a piece-wise linear function that simply clips the activation rather than squashing it.

If no non-linearity is introduced by squashing or clipping, the network loses much of its computational power, becoming a simple matrix multiplication operation from linear algebra.

Alternative calculation models in neural networks include models with loops, where some kind of time delay process must be used, and "winner takes all" models, where the neuron with the highest value from the calculation fires and takes a value 1, and all other neurons take the value 0.

Typically the weights in a neural network are initially set to small random values. This represents the network knowing nothing; its output is essentially a random function of its input. As the training process proceeds, the connection weights are gradually modified according to computational rules specific to the learning algorithm being used. Ideally the weights eventually converge to values allowing them to perform a useful computation. Thus it can be said that the neural network commences knowing nothing and moves on to gain some real knowledge, though the knowledge is sub-symbolic.

ARCHITECTURE OF NEURAL NETWORKS

FEED-FORWARD NETWORKS:-

 Feed-forward ANNs allow signals to travel one way only; from input to output. There is no feedback (loops) i.e. the output of any layer does not affect that same layer. Feed-forward ANNs tend to be straight forward networks that associate inputs with outputs. They are extensively used in pattern recognition. This type of organization is also referred to as bottom-up or top-down. ex.,  Fault Diagnosis systems.

FEEDBACK NETWORKS:-

Feedback networks can have signals traveling in both directions by introducing loops in the network. Feedback networks are very powerful and can get extremely complicated. Feedback networks are dynamic; their 'state' is changing continuously until they reach an equilibrium point. They remain at the equilibrium point until the input changes and a new equilibrium needs to be found. Feedback architectures are also referred to as interactive or recurrent, although the latter term is often used to denote feedback connections in single-layer organizations. Ex., Radiosity for Virtual Reality Systems (ROVER)

TRAINING OF ARTIFCIAL NEURAL NETWORKS:-

A neural network has to be configured such that the application of a set of inputs produces (either 'direct' or via a relaxation process) the desired set of outputs. Various methods to set the strengths of the connections exist. One way is to set the weights explicitly, using a priori knowledge. Another way is to 'train' the neural network by feeding it teaching patterns and letting it change its weights according to some learning rule.

We can categorize the learning situations in three distinct sorts. They are:

• Supervised learning or Associative learning in which the network is trained by providing it with input and matching output patterns. These input-output pairs can be provided by an external teacher, or by the system which contains the neural network (self-supervised).

 

This method has two sub varieties called auto-associative and hetero-associative. In auto-associative learning, the target values are the same as the inputs, whereas in hetero-associative learning, the targets are generally different from the inputs.

One of the most commonly used supervised NN model is back propagation network that uses back propagation learning algorithm. Back propagation (or back prop) algorithm is one of the well-known algorithms in neural networks.

• Unsupervised learning method is not given any target value. Unsupervised learning or Self-organization in which an (output) unit is trained to respond to clusters of pattern within the input. In this paradigm the system is supposed to discover statistically salient features of the input population. Unlike the supervised learning paradigm, there is no a priori set of categories into which the patterns are to be classified; rather the system must develop its own representation of the input stimuli. A desired output of the network is unknown. During training the network performs some kind of data compression such as dimensionality reduction or clustering. The network learns the distribution of patterns and makes a classification of that pattern where, similar patterns are assigned to the same output cluster.

 Kohonen network is the best example of unsupervised learning network. According to Sarle (1997) Kohonen network refers to three types of networks that are Vector Quantization, Self-Organizing Map and Learning Vector Quantization. 

• The third major category of learning that will be discussed is termed "reinforcement learning". As mentioned earlier, reinforcement learning lies between supervised and unsupervised learning. It is often called learning with a critic rather than learning with a teacher as the feedback is evaluative (right or wrong) rather than instructive (desired output). Reinforcement learning in neural networks has its roots in behavioral psychology. Here the learning machine does some action on the environment and gets a feedback response from the environment. The learning system grades its action good (rewarding) or bad (punishable) based on the environmental response and accordingly adjusts its parameters. Generally, parameter adjustment is continued until an equilibrium state occurs, following which there will be no more changes in its parameters. The self organizing neural learning may be categorized under this type of learning.

APPLICATIONS OF NEURAL NETWORK:-

• Signal processing: suppress line noise, with adaptive echo canceling, blind source separation
• Control: e.g. backing up a truck: cab position, rear position, and match with the dock get converted to steering instructions. Manufacturing plants for controlling automated machines.
• Siemens successfully uses neural networks for process automation in basic industries, e.g., in rolling mill control more than 100 neural networks do their job, 24 hours a day
• Robotics - navigation, vision recognition
• Pattern recognition, i.e. recognizing handwritten characters, e.g. the current version of Apple's Newton uses a neural net
• Medicine, i.e. storing medical records based on case information
• Speech production: reading text aloud (NETtalk)
• Speech recognition
• Vision: face recognition , edge detection, visual search engines
• Business, eg.. rules for mortgage decisions are extracted from past decisions made by experienced evaluators, resulting in a network that has a high level of agreement with human experts.
• Financial Applications: time series analysis, stock market prediction
• Data Compression: speech signal, image, e.g. faces
• Game Playing: backgammon, chess, ROBOCUP.

Robocup: Robot World Cup:-

The RoboCup Competition pits robots (real and virtual) against each other in a simulated soccer tournament. The aim of the RoboCup competition is to foster an interdisciplinary approach to robotics and agent-based AI by presenting a domain that requires large-scale cooperation and coordination in a dynamic, noisy, complex environment. RoboCup has three different leagues to-date. The Small and Middle-Size Leagues involved physical robots; the Simulation League is for virtual, synthetic teams. This work focus on building softbots for the Simulation League. Machine Learning for Robocup involves:

1.The training of player in the process of making the decision of whether (a) to dribble the ball; (b) to pass it on to another team-mate; (c) to shoot into the net.

 2. The training of the goalkeeper in process of intelligent guessing of how the ball is going to be kick by the opponents. Complexities arise when one opponent decides to pass the ball to another player instead of attempting a score.

3. Evolution of a co-operative and perhaps unpredictable team.

CONCLUSION:-

The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful. Furthermore there is no need to device an algorithm in order to perform a specific task; i.e. there is no need to understand the internal mechanisms of that task. They are also very well suited for real time systems because of their fast response and computational times which are due to their parallel architecture.

Neural networks also contribute to other areas of research such as neurology and psychology. They are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain.

Perhaps the most exciting aspect of neural networks is the possibility that some day 'conscious' networks might be produced. There is a number of scientists arguing that consciousness is a 'mechanical' property and that 'conscious' neural networks are a realistic possibility.

Finally, to state that even though neural networks have a huge potential we will only get the best of them when they are integrated with computing, AI, fuzzy logic and related subjects.

About the Author

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Neural Networks Theory $89.48 "Neural Networks Theory is a major contribution to the neural networks literature. It is a treasure trove that should be mined by the thousands of researchers and practitioners worldwide who have not previously had access to the fruits of Soviet and Russian neural network research. Dr. Galushkin is to be congratulated and thanked for his completion of this monumental work; a book that only he could write. It is a major gift to the world." Robert Hecht Nielsen, Computational Neurobiology, University of California, San Diego "Professor Galushkins monograph has many unique features that in totality make his work an important contribution to the literature of neural networks theory. He and his publisher deserve profuse thanks and congratulations from all who are seriously interested in the foundations of neural networks theory, its evolution and current status." Lotfi Zadeh, Berkeley, Founder of Fuzziness "Professor Galushkin, a leader in neural networks theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization, concepts that are solidly grounded in Russian tradition. His theory is expansive: covering not just the traditional topics such as network architecture, it also addresses neural continua in function spaces. I am pleased to see his theory presented in its entirety here, for the first time for many, so that the both theory he developed and the approach he took to understand such complex phenomena can be fully appreciated." Sun-Ichi Amari, Director of RIKEN Brain Science Institute RIKEN

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An Introduction to Neural Networks $24.98 Choice Outstanding Academic Title, 1996. An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas. Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject. The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.

Biological Neural Networks $151.48 "Biological Neural Networks" presents a novel conceptual framework for neurobiology achieved by the application of control theory. This new paradigm provides unifying principles for understanding the functional construction of the nervous system. Konstantin Baev argues forcefully that all hierarchical levels of the nervous system are built according to the same functional principles, which are shown to underlie the highest forms of brain function. Each network hierarchy is structurally and functionally organized in such a way that a lower control system in the nervous system becomes the controlled object for a higher one, and each level of control possesses a model of behavior of its controlled object. The book brings together for the first time the fields of neural networks (with its computational capabilities), control theory (with its hierarchical machinery), and neurobiology (with its plethora of enigmatic physiological functions, such as inborn and acquired automatic behavior). The function of the cerebellum, the limbic system, and the cortico-basal ganglia-thalamocortical loops are analyzed within this new hierarchical framework. Clinical applications include an original explanation of Parkinson's disease and suggested mechanisms of alleviating its symptoms by conducting functional neurosurgical procedures. Baev writes with an interdisciplinary readership in mind. Neuroscientists, computer specialists and mathematicians, physicists and clinicians devoted to deciphering the way brain works will all find it fascinating and stimulating reading.

Modeling Brain Function $39.98 Exploring one of the most exciting and potentially rewarding areas of scientific research, the study of the principles and mechanisms underlying brain function, this book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. Substantial progress in understanding memory, the learning process, and self-organization by studying the properties of models of neural networks have resulted in discoveries of important parallels between the properties of statistical, nonlinear cooperative systems in physics and neural networks. The author presents a coherent and clear, nontechnical view of all the basic ideas and results. More technical aspects are restricted to special sections and appendices in each chapter.

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A Masculine Cologne Can Attract women

Tantalize her with scents

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One area that guys don't take advantage of enough is the power of smell with a woman. Scents and smells are very important to them, and it's been scientifically proven that women are much more sensitive than men to odors.

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One area that guys don't take advantage of enough is the power of smell with a woman. Scents and smells are very important to them, and it's been scientifically proven that women are much more sensitive than men to odors.

 

One of the most important reasons scents are good for engaging a woman's attraction for you is that your sense of smell is connected to the most basic part of your brain, the part that was formed long before your ability to reason and think. This was because our early ancestors had to be able to react quickly to scents on the wind that could indicate a threat, such as a predator.

 

Smells have a way of bringing back memories. Have you ever had the experience of smelling something and having a complete flashback to something you remember from your childhood? It happens all the time, and it's even more potent when you've associated a smell with something, like the smell of burning leaves in autumn, or the smell of homemade pizza. These smells evoke a state in us that pulls us back to the experiences we had with that smell.

 

I've walked into old houses before, ones that were humid and smelled of old bread and tea, and I had these flashes of walking into my grandmother's house and the same odors there. It's a little freaky how real it feels at times.

 

The way you use this with a woman is to find out what scents she indulges in when she wants to relax, or get passionate. I find that getting her into a candle store, or a bath-and-bodyworks type store is the best way. You get her to smell different things, and notice what she prefers. Vanilla is usually a good scent that you can almost bet she'll like. Find out what she likes to take a bath with.

 

Scents for women can usually be broken up into two categories: musks and floral. Musky scents are like heavy perfumes, like Obsession. Floral scents are the ones that are, obviously, more like flowers. If she wears any kind of perfume, you can tell which she prefers by smelling what she's wearing.

 

Take care with your choice of cologne. One tactic is to find out what cologne her dad used to wear and use that, but I find this strategy highly variable in terms of the response you'll get. Better to choose the cologne that she remarks on and likes. Make it a point to put on too little rather than too much. You want her trying to get closer to you, not running and gagging from the smell. Have her give you a critique. "Hey, I just got this new cologne, but I'm not sure if I like it. Tell me what you think." And she'll have to lean in close to you to get a whiff. If she likes it, she'll be back for more.

(And don't go cheap on your cologne. It's worth it.)

 

When you can get her sense of smell engaged, you are taking a shortcut to a part of her mind that responds in an almost primal way. She can't reason or rationalize with it. You can get her in a state of your own choosing by choosing the right scent.

 

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Carlos Xuma is a well-known expert in the dating-seduction related field and an author of bestselling titles such as "Secrets of the Alpha Male", "Approach Women - NOW!" and "Alpha Immersion".

 

Click here for more information on Carlos Xuma:

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About the Author

Alex Coulson has been a core leader in the ever evolving dating industry, he holds cutting-edge dating workshops/courses every weekend around Australia and regularly holds dating seminars on attracting women internationally.

For more information and to claim access to Alex Coulson’s “5 Step System” Audio E-course for FREE (limited time only) head over to http://www.alexcoulson.com

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Do You Have Depression From Female Hormones?

About Depression—Depression can happen to anyone of any age and is a common, yet potentially serious, symptom of menopause and female hormones.. Clinical depression is a severe period of sadness characterized by more than two weeks of extremely low moods that affect how a person feels, thinks, and acts.

Symptoms of Depression—There are three categories of depression (physical, emotional, and behavioral and at least five symptoms must be present for no less than two weeks and one of those five must either be persistent feeling of sadness or loss of interest or pleasure. Female hormones are often the underlying cause in menopausal aged women.

Physical Symptoms: Fatigue, decreased energy, overeating, appetite loss, insomnia, early-morning, wakefulness.

Emotional Symptoms: Persistent sad, anxious, or empty feelings, feelings of hopelessness and/or pessimism, feeling of guilt, worthlessness and/or helplessness. Female hormones can be treated with drugs or natural progesterone creams. Many health articles are written about the impact of female hormones on depression.

Behavioral Symptoms: Loss of interest in activities or hobbies once pleasurable including sex, difficulty concentrating, difficulty remembering details, difficulty making decisions, and neglecting responsibilities.

Causes of Depression—The underlying cause of depression in menopausal women has to do with female hormone imbalance, especially decreased levels of estrogen. As women approach menopause, their estrogen levels begin to drop off. This female hormone plays a big part in regulating brain functions, especially chemicals that influence mood, such as serotonin and Cortisol. Decreasing levels of estrogen during menopause can also cause other physical and mental symptoms, such as hot flashes, and anxiety, which lead to depression. One of the early menopause symptoms is depression and is directly tied to the imbalance of female hormones.

Other causes include biochemical, genetic, personality, environmental factors and disease.

Treatments For Depression—As usual, the least invasive option is lifestyle changes, which involves regular exercise, eating healthy, and practicing mind-body techniques such as meditation or yoga.

Simply by including three 30-minute sessions of physical activity, such as walking, jogging, swimming, or cycling into a weekly routine can greatly relieve the milder symptoms of depression.

Similarly, eating healthily will also help to stabilize mood swings and alleviate depression.

The most effective approach to relieve depression in menopausal women is directly at the problem itself—female hormone imbalance. A variety of natural and alternative supplements exist that may be able to address this imbalance.

One such natural alternative would be a natural balancing cream. Click here for an excellent, natural, botanically-based balancing cream. For more prolonged or severe cases, it may be necessary to seek the advice of a healthcare professional.

We hope you enjoyed this article.

About the Author

John Russell of IH Distribution, LLC brings you health, anti-aging and skin care products from around the world. Find fabulous skin care tips and great articles on a wide range of topics for women at our Skin Care Blog and http://www.hormones-beauty-health.com. In addition to skin care, they are also a great source for As Seen On TV Products. Don't forget to check out their newly re-launched newsletter - filled with information you won't find anywhere else.

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The Truth About Food Aggression in Dogs

Are you just plain fed up?  Does feeding your dog turn into a nightmare of aggressive dog behavior if anyone in the household comes near him while he's eating?  If your pooch is displaying food aggression in dogs, this serious dog behavior problem needs to be stopped before your dog attacks you or someone in your family.

Dog Food Aggression Is Instinctive Behavior For Canines

In the wild, a dog is protective of his food, his mate, and his territory.  Aggressive behavior in dogs is necessary for their survival.  The most aggressive dog is the pack leader, or alpha male, and he enforces his position by being the dominant dog in the pack. 

However, your dog shouldn't be the pack leader in your home.  If he is, you're in for trouble, because he thinks he's the alpha male, and like he would in the wild, he'll enforce his position by using aggressive dog behavior.  This is a dominance issue that won't go away on its own.  You must take steps to deal with his aggressive behavior before it reaches a dangerous level.

Causes of Food Aggression In Dogs

Most of us have laughed at a "cute" little puppy who's aggressively guarding his food.  Don't do it!  This is a serious training mistake many dog owners make.  Instead of encouraging this kind of canine possession aggression, you need to nip this problem in the bud.

In your dog's mind, he's confused about who the pack leader is.  If you don't take the role of the alpha male (even if you're female), there's a leadership vacuum, to your dog's way of thinking.  If you don't step into the role, he will.  And he won't be nice about enforcing his position in your household.

Since he considers himself the top dog, he thinks he can take whatever he wants from whoever is lower in rank than he is.  The reality is that the family dog should be at the bottom of the heap, not the top.  Anyone in the household should be able to take anything away from him, even his food.  Of course, you're not really going to do this, but he needs to know where he stands in your household pack. 

Dog Food Aggression Training

With a puppy, you'll make it clear that having people or other animals around when he's eating is OK.  Do this right from the beginning, and you'll probably never have a problem with this type of aggressive dog behavior.  Anyone in the family should be able to safely stand right next to him at any time while he's eating.  If this isn't the case, you need to take control of his food.

It's always a good idea to feed your dog after you and your family have finished eating.  Your dog will instinctively understand that the alpha dog always eats first.  This reinforces your position and your family members' positions in the pack in relationship to him.

Require that your dog sit and stay quietly while you're fixing his food.  If he whines and jumps around and demands food, don't give in.  He needs to earn his food by behaving in a non-aggressive manner. 

Never let your dog "win" his food by growling at you.  This only rewards his bad dog behavior.

If food aggression in dogs is already a serious problem in your household, a dog behavior course may be your next step.  Look for a course that includes a consultation with an experienced dog trainer, and that has a forum where you can get advice from other dog owners.

Now that you know what to do, don't delay in taking steps to control dog food aggression.

About the Author

Darlene Norris worked at a vet clinic and an animal shelter, and has had lots of experience dealing with aggressive dogs. Visit her new website, No More Bad Dogs, to learn more about food aggression in dogs, and discover which dog training course she recommends at http://NoMoreBadDogs.com

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Aggression Antisocial Behavior and Violence Among Girls $37.5 From leading authorities, this book traces the development of female aggression and violence from early childhood through adulthood...

simon baron cohen the essential difference

The Essential Difference $13.48 We all know the opposite sex can be a baffling, even infuriating, species. Why do most men use the phone to exchange information rather than have a chat? Why do women love talking about relationships and feelings with their girlfriends while men seem drawn to computer games, new gadgets, or the latest sports scores? Does it really all just come down to our upbringing? In The Essential Difference, leading psychologist Simon Baron-Cohen confirms what most of us had suspected all along: that male and female brains are different. This groundbreaking and controversial study reveals the scientific evidence (present even in one-day-old babies) that proves that female-type brains are better at empathizing and communicating, while male brains are stronger at understanding and building systems-not just computers and machinery, but abstract systems such as politics and music. Most revolutionary of all, The Essential Difference also puts forward the compelling new theory that autism (and its close relative, Asperger's Syndrome) is actually an example of the extreme male brain. His theory can explain why those who live with this condition are brilliant at analyzing the most complex systems yet cannot relate to the emotional lives of those with whom they live. Understanding our essential difference, Baron-Cohen concludes, may help us not only make sense of our partners' foibles, but also solve one of the most mysterious scientific riddles of our time.

Essential Difference Male and Female Brains and the Truth About Autism $15.99 ISBN-13: 9780465005567 ISBN-10: 046500556X Title: Essential Difference Male and Female Brains and the Truth About Autism. Author: Simon Baron-Cohen.

Autism Researchers: Christopher Gillberg, Andrew Wakefield, Vilayanur S. Ramachandran, Bruno Bettelheim, Mark Geier, Simon Baron-Cohen $19.99 Autism Researchers: Christopher Gillberg, Andrew Wakefield, Vilayanur S. Ramachandran, Bruno Bettelheim, Mark Geier, Simon Baron-Cohen

Sacha Baron Cohen $11.05 Sacha Baron Cohen

The Essential Difference: Male And Female Brains And The Truth About Autism $12.16 We all know the opposite sex can be a baffling, even infuriating, species. Why do most men use the phone to exchange information rather than have a chat? Why do women love talking about relationships and feelings with their girlfriends while men seem drawn to computer games, new gadgets, or the latest sports scores? Does it really all just come down to our upbringing? In The Essential Difference, leading psychologist Simon Baron-Cohen confirms what most of us had suspected all along: that male and female brains are different. This groundbreaking and controversial study reveals the scientific evidence (present even in one-day-old babies) that proves that female-type brains are better at empathizing and communicating, while male brains are stronger at understanding and building systems-not just computers and machinery, but abstract systems such as politics and music. Most revolutionary of all, The Essential Difference also puts forward the compelling new theory that autism (and its close relative, Asperger's Syndrome) is actually an example of the extreme male brain. His theory can explain why those who live with this condition are brilliant at analyzing the most complex systems yet cannot relate to the emotional lives of those with whom they live. Understanding our essential difference, Baron-Cohen concludes, may help us not only make sense of our partners' foibles, but also solve one of the most mysterious scientific riddles of our time.

Old Haberdashers: Sacha Baron Cohen, Damon Hill, Jason Isaacs, Richard Wright, Nicholas Serota, Haberdashers' Aske's Boys' School, Simon $19.99 Old Haberdashers: Sacha Baron Cohen, Damon Hill, Jason Isaacs, Richard Wright, Nicholas Serota, Haberdashers' Aske's Boys' School, Simon

Baron Cohen And Fisher Married $10 Baron Cohen And Fisher Married - Hollywood News

Autism: The Facts - Simon Baro $17.75 ISBN-13: 9780192623287 ISBN-10: 0192623281 Title: Autism: The Facts - Simon Baro. Author: Simon Baron-Cohen, Patrick Bolton.

LEONARD COHEN: THE ESSENTIAL $19.98 LEONARD COHEN: THE ESSENTIAL

The Essential Leonard Cohen $9.93 The Essential Leonard Cohen

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The Many Faces of Sacha Baron Cohen $29.94 The Many Faces of Sacha Baron Cohen explores the surprising political resonance of British comedian Sacha Baron Cohen''s portrayals of Borat, Ali G, and Bruno. The book examines the political underpinnings of Baron Cohen''s humor, the cultural ramifications of his ethnically charged satire, and the global implications of his various personae.