Auto association, neural Network Model |Artificial neural network |Biology |IQ learning
Neural networks consist of nodes and connections There are multiple layers: An input layer, where information enters, one or more hidden layers, where information is transformed, and an output layer where the final prediction or results is presented. The connections have different strengths, also referred to as weights. During the training of the model, these weights are constantly updated. This is how the model learns particular features of the input information, which helps it to optimize the output. Neural networks are not only useful for neuroscience. Their biggest applications happen outside of neuroscience, for various reasons if you have any questions feel free to ask, I will respond to your questions #autoassociation #neuralnetworkmodel #Biomasterrr #neural network model
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Auto association, neural Network Model |Artificial neural network |Biology |IQ learning
- 1. 1. NEURAL NETWORK Model 2. Auto Association 3. Artificial Neural Network.
- 2. • According to this model, “the connections between neurons are the source of memories” • The basis of it is that neural networks connect and interact to store memories by modifying the strength of the connections between neural units. Neural Network Model
- 3. • This model is based on the concept of connectionism “Cells that fire together wire together” • In 1961, Karl Steinbuchput forwardthe idea for a neural network that would display association. • Such circuits are now referred to as artificial neural networks. Continued…….
- 4. AUTO-ASSOCIATION 1. the single axon is frequently observed to branch into two or more strands When this happens, the main route is still referred to as the axon, while the subsidiary conduits are known as Axon Collaterals. 2. There is an important sub-class of the latter, namely those which arch back and form synaptic contacts with the dendrites of the very neurons from which they originated. These are known as Recurrent Collaterals.
- 5. • In this way the network will be able to identify input patterns to which it has previously been exposed. Continued…….
- 6. AUTO-ASSOCIATION An auto-associative neural network; the key feature is the presence of recurrent collaterals. An auto-associative neural network in which learning has taken place; the size of each (excitatory) synapse denotes its strength.
- 7. PRESENTED BY MS. IQRA IQ LEARNING (YOUTUBE CHANNEL) LIKE-SUBSCRIBE-SHARE