👉 The CF fluid, or Continuous Flooding, is a key component in the context of Continuous Flow Neural Networks (CFNNs), which are a type of deep learning model designed to mimic the brain's information processing. In CFNNs, the "fluid" refers to a continuous stream of artificial neurons that are activated and deactivated in response to input data, much like biological neurons. This fluid is typically a simple activation function, such as a sigmoid or tanh function, which determines the output of each neuron based on the weighted sum of its inputs. The CF fluid allows the network to dynamically adjust its responses to varying input patterns, enabling it to learn complex temporal dependencies and patterns efficiently. By continuously updating the neuron activations, CFNNs can simulate the brain's ability to process information over time, making them particularly useful for tasks like sequence prediction and time-series analysis.
