Notes

Predictive Coding Predictive Coding (also known as predictive processing) is a theory of brain function in which the brain is constantly generating and updating a mental model of the environment. The model is used to generate predictions of sensory input that are compared to actual sensory input. This comparison results in prediction errors that are then used to update and revise the mental model. In short: the brain tries to predict the next input to our network.

Predictive Coding: Static Identifying a letter in word that you already know allows you to be faster and more accurate, as you are exploiting predictive capabilities. So, if we have a "high-level" description (prior knowledge) of an object, we are better in describing it.

Predictive Coding: Circuit

The left figure represents the inhibiting feedback prediction mechanism in the visual cortex. Rao & Ballard 1999 paper represents the foundational paper in predictive coding. They built this simple circuit to explain some of the effects involved in prediction and integration of top-down knowledge and bottom-up sensory stimuli. If your predictions correctly matches the input, the inhibitory connections make sure that they cancel out.

Predictive Coding: Effects Predictive Coding can be used to denoise images, which is similar to what can be done with an Autoencoder.

Predictive Coding: Supervised Learning

Predictive Coding in Biology: Circuits How can I take the Rao & Ballard circuit and map it into something in the cortex, knowing the connections between layers. The Thalamus fits into L4, then L4 fits into L2/3 and so forth... So, we can think of layer 4 as the "X" in the Rao & Ballard circuit that integrates from L5/6 and from the FF connection. L2/3 is the most recurrent part of the brain, which takes more time.

Predictive Coding in Biology: Experiment They took a mice and made him run through a VR setup where walls show lines (grates), they can adjust the lines to make the mice think he is running or keep them still so they move only when the mice is effectively running (mismatch), or also to make them move such that they look still even when the mice is running.

They put some markers in neurons, which allowed the identification of different types of neurons: the orange neuron seems to fire when there is no visual flow but the mice runs and, when "things" match, it shows a lower response. They also identified a neuron that fires in the presence of visual flow but no running. Then, they draw correlation plots which show that the orange neuron correlate negatively with visual flow, the black and grey neurons don't correlate with the visual flow, while the blue correlates positively. They proposed a circuit where you have sensory inputs (visual flow), predictions (running). And they evidence some predictions happening as the predictive coding circuit postulates.