Feedforward neural network

<p>Feedforward refers to recognition-inference architecture of neural networks. <a href="/facts/Artificial_neural_network/6V1jMlkx">Artificial neural network</a> architectures are based on inputs multiplied by weights to obtain outputs (inputs-to-output): feedforward. <a href="/facts/Recurrent_neural_network/bx7hBVB1">Recurrent neural networks</a>, or neural networks with loops allow information from later processing stages to feed back to earlier stages for sequence processing. However, at every stage of inference a feedforward multiplication remains the core, essential for backpropagation or backpropagation through time. Thus neural networks cannot contain feedback like <a href="/facts/Negative_feedback/G9byVgpN">negative feedback</a> or <a href="/facts/Positive_feedback/eFGatCpF">positive feedback</a> where the outputs feed back to the <i>very same</i> inputs and modify them, because this forms an infinite loop which is not possible to rewind in time to generate an error signal through backpropagation. This issue and nomenclature appear to be a point of confusion between some computer scientists and scientists in other fields studying brain networks.
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Feedforward neural network open-in-new

Feedforward neural network