"A neural network can refer to either a neural circuit of biological neurons (sometimes also called a biological neural network), or a network of artificial neurons or nodes in the case of an artificial neural network."
The complex web of connections that allows the brain to process and integrate information. Learn about how neural networks are formed, how they process information, and how they can be studied.
Neurons: The basic building block of the nervous system, neurons are specialized cells that transmit information.
Synapses: The junction between two neurons, where information is transmitted through chemical signals.
Action Potential: The electrical signal that travels along the axon of a neuron, allowing for communication between neurons.
Neurotransmitters: Chemical substances released by neurons to communicate with other neurons or with muscles, glands, or other tissues.
Neural Networks: A collection of neurons and synapses that work together to process information.
Feedforward Networks: Neural networks that process information in a one-way, non-cyclic manner.
Recurrent Networks: Neural networks that allow for cyclic or feedback connections, allowing for the processing of temporal or sequential data.
Convolutional Networks: Neural networks used specifically for processing visual data, such as images or videos.
Deep Learning: Neural networks that have more than one hidden layer, allowing for the processing of complex inputs.
Backpropagation: A method used to train neural networks by iteratively adjusting the weights of connections between neurons based on errors in the output of the network.
Gradient Descent: A method used to optimize the weights of connections between neurons by iteratively adjusting them in the direction of steepest descent of a cost function.
Activation Functions: Mathematical functions applied to the output of neurons to introduce nonlinearity and allow for the processing of complex inputs.
Dropout: A regularization technique used in neural networks to prevent overfitting by randomly dropping out some of the neurons during training.
Batch Normalization: A technique used to normalize the inputs of each layer in a deep neural network, improving its performance and robustness.
Autoencoders: Neural networks used for unsupervised learning, where the goal is to reconstruct the input from a compressed representation.
Reinforcement Learning: A type of learning used in neural networks that involves learning by trial and error in response to rewards or punishments.
GANs: Generative Adversarial Networks are a type of neural network used for generating high-quality images, music, or text.
Transfer Learning: A technique used in neural networks where pre-trained models are used to build new models on new datasets by adapting their parameters to the new data.
Neuroplasticity: The ability of the nervous system to adapt and change over time in response to new experiences.
Brain-Computer Interfaces: Devices that allow for direct communication between the brain and a computer, allowing for control of devices or communication without the use of muscles.
Artificial Neural Networks (ANNs): ANNs are mostly used for supervised learning. It follows a feedforward model where the data goes from input to output through hidden layers. ANNs can also be categorized as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Deep Belief Networks (DBNs).
Convolutional Neural Networks (CNNs): CNNs are mostly used for image and audio data processing. CNNs follow convolutional and pooling layers to capture local patterns and generate fewer but strong features. It uses backpropagation for learning and training.
Recurrent Neural Networks (RNNs): RNNs are mostly used for sequential data processing like speech recognition, language translations, etc. RNNs use LSTM (Long Short-Term Memory) units or GRU (Gated Recurrent Unit) to avoid the vanishing gradient problem that occurs in deep learning.
Autoencoder Neural Networks: Autoencoder Neural Networks are mostly used for unsupervised learning. It works by reducing the input data into a compressed representation and outputting it again. Autoencoder Neural Networks are used in data dimensionality reduction, feature extraction, data denoising, and anomaly detection.
Deep Belief Networks (DBNs): DBNs are mostly used for unsupervised learning methods like restricted Boltzmann machines (RBMs), Contrastive Divergence (CD) algorithms, and Deep Belief Networks (DBNs). It can be used for classification, dimensionality reduction, and feature extraction.
Boltzmann Machines (BMs): BMs are mostly used for identifying patterns in data. It works by simulating the pattern recognition processes of the human brain, and it can be used for tasks like image or speech pattern recognition, predicting the outcomes of future events, or modeling complex systems.
"Artificial neural networks are used for solving artificial intelligence (AI) problems."
"They model connections of biological neurons as weights between nodes."
"A positive weight reflects an excitatory connection."
"Negative values mean inhibitory connections."
"All inputs are modified by a weight and summed."
"This activity is referred to as a linear combination."
"An activation function controls the amplitude of the output."
"For example, an acceptable range of output is usually between 0 and 1."
"Or it could be −1 and 1."
"These artificial networks may be used for predictive modeling, adaptive control, and applications where they can be trained via a dataset."
"Self-learning resulting from experience can occur within networks."
"Networks [can] derive conclusions from a complex and seemingly unrelated set of information."
"A neural circuit of biological neurons is sometimes also called a biological neural network."
"They model connections of biological neurons as weights between nodes."
"Negative values mean inhibitory connections."
"All inputs are modified by a weight and summed."
"An activation function controls the amplitude of the output."
"For example, an acceptable range of output is usually between 0 and 1."
"They can derive conclusions from a complex and seemingly unrelated set of information."