Biological Neural Network.pptx

Biological neural Network

1. Biological Intelligence and Neural Networks

2. What is Biological Intelligence Neural Networks ? The Biological Neural N etwork is simulation of human brain. Or simulation living organisms Biological neural networks refer to the networks of neurons found in the biological brain, while in Artificial Intelligence(AI) the neural network is a type of machine learning model that is inspired by the structure and function of biological neural networks. For example The birds had inspired humans to create airplanes, and the four-legged animals inspired us to develop cars.

3. Layout of a Biological Neural Network 1.Cerebrum largest part (right and left) 2.Cerebellum under the Cerebrum 3.Brainstem center part connection with both

4. What are Neural Networks? The Neural network is a subset of Machine Learning and the heart of deep learning Algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. It is composed of layers of interconnected "neurons" which process and transmit information. Neural networks are used for a variety of tasks, such as image and speech recognition, natural language processing, and decision making.

5. What are Artificial Neural Networks? Artificial neurons are crude approximations of the neurons found in brains. They may be physical devices, or purely mathematical constructs. Artificial Neural Networks (ANNs) are networks of artificial neurons, and hence constitute crude approximations to parts of functioning brains. They may be physical devices, or simulated on conventional computers. Artificial Neural Network model involves computations and mathematics, which simulate the human–brain processes. Many of the recently achieved advancements are related to the artificial intelligence research area such as image and voice recognition, robotics, and using ANNS. The (ANN) models have the specific architecture format, which is inspired by a biological nervous system. Like the structure of the human brain, the ANN models consist of neurons in a complex and nonlinear form. The neurons are connected to each other by weighted links. All the processes in ANN models, such as data collection and analysis, network structure design, number of hidden layers, network simulation,

6. What are Artificial Neural Networks used for? As with the field of AI in general, there are two basic goals for neural network research: Brain modelling : The scientific goal of building models of how real brains work. This can potentially help us understand the nature of human intelligence, formulate better teaching strategies, or better remedial actions for brain damaged patients. Artificial System Building : The engineering goal of building efficient systems for real world applications. This may make machines more powerful, relieve humans of tedious tasks, and may even improve upon human performance.

7. Some Current Artificial Neural Network Applications Brain modelling Models of human development – help children with developmental problems Simulations of adult performance – aid our understanding of how the brain works Neuropsychological models suggest – remedial actions for brain damaged patients Real world applications Financial modelling – predicting stocks, shares, currency exchange rates Other time series prediction – climate, weather, airline marketing tactician Computer games – intelligent agents, backgammon, first person shooters Control systems – autonomous adaptable robotics, microwave controllers

8. Biological Neural Networks vs Artificial Neural Networks The human brain consists of about 86 billion neurons and more than 100 trillion synapses. In artificial neural networks, the number of neurons is about 10 to 1000. But we cannot compare biological and artificial neural networks’ capabilities based on just the number of neurons. There are other factors also that need to be considered. There are many layers in artificial neural networks, and they are interconnected to solve classification problems

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