Lecturer
Artificial Neural Network
Definition: An ANN is an information-processing system that shares certain characteristics with biological neural networks. These networks are modeled after human cognition or neural biology.
Key Characteristics:
- Information processing occurs at many simple elements called neurons.
- Signals are passed between neurons over connection links.
- Weights: Each connection link has an associated weight. In a typical neural network, this weight multiplies the signal transmitted.
- Activation Function: Each neuron applies a usually nonlinear activation function to its net input (sum of weighted input signals) to determine its output signal.
Neural Network Structure:
A neural network is defined by:
- The pattern of connections between the neurons (its architecture).
- The method for determining the weights on the connections (its training or learning algorithm).
- The activation function it uses.
Biological Neural Networks
- A biological neuron has three primary components:
- Dendrites: Receive signals from other neurons.
- Soma (cell body): The main part of the neuron where signals are processed.
- Axon: Sends signals to other neurons.
- Dendrites receive signals through electric impulses that are transmitted across a synaptic gap via a chemical process.
- A chemical transmitter modifies the incoming signal, typically by scaling the frequency of signals received. This process is analogous to how weights in an artificial neural network adjust the strength of the signals.
The diagram shows a neuron with:
- Dendrites receiving signals from another neuron’s axon.
- Synaptic gaps where the signal passes chemically between neurons.
- The soma processes the signals, and the axon transmits the processed signal to other neurons.
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